Reserved topic scholarships | Doctorate Program in Industrial Innovation
 

Reserved topic scholarships

The Doctorate Program in Industrial Innovation offers mainly 2 types of 3-year PhD positions:

  • Standard PhD positions with scholarships financed by the companies;
  • PhD Executive positions, which are without scholarships and reserved to the partner companies' employees who maintain their status (and salary) within their company. The applicants can be either already employed by the company, or they can be employed after the selection process, but before the PhD Program starts. The financial conditions are defined directly between the company and the applicant.

37th Cohort - Intake year 2021

Scholarships

Funding Body

N. 

Research topic

Link

Consiglio Nazionale delle Ricerche (CNR) and Fonderie Ariotti S.p.A.

1

A - Designing against failures resulting from static and time-varying loading in thick-walled components made of ductile cast iron

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Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT) and Athonet S.r.l.

1

B - Automated Configuration and Orchestration of Beyond-5G Mobile Core Networks

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Sinapsi S.r.l

1

C - AI4Energy:Towards a new generation of AI-driven recommendation tools for the energy market

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FBK-Roche S.p.A.

1

D - Application of natural language processing technologies to clinical cases

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FBK-UFI Innovation Center S.r.l.

1

E - Development of a novel membrane based on anionic exchange for use in the electrolysis process

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FBK-Enphos S.r.l.

1

F - Study of Anion exchange membrane electrolyzers: improvements of the performance with the use of innovative functional materials 

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Adige S.p.A.

1

G - Ontology-driven support to fault diagnosis of manufacturing machines based on service mail flows and repair records 

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FBK-SNAM S.p.A.

1

H - Investigation of the direct ammonia synthesis and its utilization in reversible HT cells

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FBK-MindEarth

1

I - Deep Learning for Understanding Visual Scenes - additional (published on 27/07/2021)

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FBK-MindEarth

1

J - Using Satellite Imagery and Deep Learning for Understanding Socio-Economic Development additional (published on 27/07/2021)

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Athonet S.r.l.

1

K - 3GPP Edge Observability and Scalability - additional (published on 30/07/2021)

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PhD Executive positions, reserved to employees of partner companies:

Company

Research Topic

Link

COSMAN S.r.l.

1

AE - Machine Learning and artificial Intelligence techniques applied to Up-to-date procurement and supply-chain methodologies for Digital Cost Management cost management

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MAVTech S.r.l.

1

BE - Development of innovative digi-tech solutions for smart farming

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SoftJam S.p.A.

1

CE - Interpretation of very large-scale conversational data

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Huawei Technology Duesseldorf GbmH

1

DE - IoT and Cloud Data security including: (1) Situation Aware, Dynamic Authorization Automotive, (2) Data-centric usage control policies for sovereign data exchange, (3) Bridging federated and decentralized identity - additional (published on 08/07/2021)

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ETC Sustainable Solutions S.r.l.

1

EE - Machine Learning Methods for Wastewater Plant Monitoring - additional (published on 14/07/2021)

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Iveco Defence Vehicles S.p.A.

1

FE - Electronic architecture and applications for enhanced vehicle control - additional (published on 29/07/2021)

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Arcoda S.r.l.

1

GE - 3D Reconstruction and optimization of indoor environments: the creation of accurate digital twins for the exploration on Extended reality devices - additional (published on 17/08/2021)

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CoRehab S.r.l.

1

HE - Implementing rehabilitation tools of REPAIRS in commercial rehabilitation video games - additional (published on 19/08/2021)

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Resonanz Capital GmbH

1

IE - Portfolio Selection and Machine Learning - additional (published on 19/08/2021)

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Consiglio Nazionale delle Ricerche (CNR) and Fonderie Ariotti S.p.A. - 1 scholarship

A - Designing against failures resulting from static and time-varying loading in thick-walled components made of ductile cast iron

Ductile Cast Irons (DCIs) are ternary Fe-C-Si alloys in which graphite forms as spheroidal particles (nodules), allowing for a good compromise between mechanical properties and a low production cost. The number of graphite nodules and their shape come from various technological factors which affect cooling rate and physicochemical state of the liquid metal. The mechanical design of components fabricated via large castings (thickness > 150 mm) of DCIs where cooling rates are very slow, has followed until now the same guidelines set up for steel components. However, DCI displays a very different static and cyclic mechanical behavior. The proposed research will bring the design of thick-walled castings beyond the current limitations dictated by the only knowledge of the static strength and by the use of design criteria suitable only for ductile metals like steels. In addition, the research will allow a deeper comprehension of the correlation between process-microstructure-mechanical properties and this will lead to a safer employment of DCI in critically stressed components. More details.

Contact: vigilio.fontanari [at] unitn.it (Vigilio Fontanari) (UniTrento), giuliano.angella [at] cnr.it (Giuliano Angella) (CNR), Danilo.Lusuardi [at] fonderieariotti.com (Danilo Lusuardi) (Fonderie Ariotti S.p.A)

Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT) e Athonet S.r.l. - 1 scholarship

B - Automated Configuration and Orchestration of Beyond-5G Mobile Core Networks

The paradigm of virtualization has been gaining momentum in the context of networking, as various network functions, which were originally implemented by dedicated hardware, are becoming entirely software-based, yielding a more flexible and dynamic design of network deployments. In this context, the network function virtualization (NFV) industry specification group (ISG) of the European Telecommunications Standards Institute (ETSI) has been working on a framework for the management and orchestration (MANO) of virtual network functions (VNFs). Other than thoroughly specifying such a framework, the ETSI ISG NFV is promoting a collaborative project to design an open-source orchestration platform called Open-Source MANO (OSM). The virtualization process is forcefully emerging also in the context of the mobile networks standardized by the 3rd Generation Partnership Project (3GPP), as the core network, which is the brain of the mobile network, is becoming more and more software-centric and hardware-agnostic. Moreover, the growing interest in private mobile networks, that are, local mobile networks owned by enterprises and meant for different industries such as, e.g., smart automotive factories, calls for an agile process to deploy core network instances on-premise and manage their lifecycle. The objective of this thesis is to enhance the network management model (comprising fault, configuration, accounting, performance, security – FCAPS) of 5G-and-beyond mobile core systems. To achieve such an objective, the candidate will devise novel strategies for the orchestration of 5G core networks which meet the identified KPIs and overcome the limitations of currently available orchestration tools. The proposed advancements with the respect to the state of the art of 5G technology shall pave the way towards the next generation of mobile networks. As an integral part of the project, the aforementioned innovations shall be applied to industry-leading mobile core networks, to show the effectiveness of the proposed approach in real deployment scenarios.

Required/Preferred Candidate Skills and Competencies: This doctoral thesis calls for an exceptionally motivated candidate, with both a research- and an implementation-oriented profile. A M.Sc. degree in Telecommunications Engineering or in Computer Science with a background on mobile wireless networks and virtualization is mandatory. Good knowledge of 3GPP mobile core networks as well as ETSI NFV/MEC standards is a plus. Previous experience with Linux distros, container platforms and their orchestration (Docker, Kubernetes), hypervisors and virtual infrastructure managers (VMware, OpenStack), network function/service orchestrators (OSM, ONAP) is a nice-to-have. Regardless the candidate’s technical skills, soft skills will also be valued, along with the willingness to learn and play with state-of-the-art tools in the mobile networks field.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student and, in particular, the intellectual property relative to any developed products and/or services that the Company may market is owned by the Company.

Contact: fabrizio.granelli [at] unitn.it (Fabrizio Granelli) (UniTrento), marco.centenaro [at] athonet.com (Marco Centenaro) (Athonet S.r.l.)

Sinapsi srl - 1 scholarship

C - AI4Energy: Towards a new generation of AI-driven recommendation tools for the energy market

SINAPSI intends to start an Industrial PhD in order to develop a suite of analysis tools capable of creating added value from energy consumption profiling users. The latter are made aware of their own energy consumption thanks to the installation of the new OPEN METER 2G (second generation). This suite of tools should NILM (Non Intrusive Load Monitoring) algorithms and validation systems such as Blockchains combined with preference elicitation and recommendation capabilities leveraging Artificial Intelligence and Machine Learning methodologies. The purpose of this suite of analysis tools is to offer services to the energy market that is moving towards a flexible demand-response management. These services will produce a high value in energy efficiency as well as in the rationalization of the use of energy and will offer to the end user awareness of his own energy consumption and suggest a model of virtuous behavior.

INNOVATIVE CONTENT

  • The project leverages the unprecedented availability of real time data on the energy flows exchanged guaranteed by the new generation of meters.
  • These real-time data pave the way to a broad range of novel applications, such as integration with home automation systems in the field of energy efficiency and energy consumption monitoring, as well as dynamic energy performance of the building;
  • This technology calls for substantial innovations in the fields of recommender systems and preference elicitation in order to provide targeted solutions combining user needs and different types of constraints.

Required/Preferred Candidate Skills and Competencies: Basic knowledge on Data Modeling, Relational databases, symbolic and machine learning AI techniques are required. Skills in energy efficiency and electrical energy are welcome. Good knowledge of English and Italian languages is required.

Contact: andrea.passerini [at] unitn.it (Andrea Passerini) (UniTrento), Stefano Rotini (Sinapsi srl)

FBK, in collaboration with Roche S.p.A.- 1 scholarship

D - Application of natural language processing technologies to clinical cases

A clinical case is a statement of a clinical practice, presenting the reason for a clinical visit, the description of physical exams, and the assessment of the patient’s situation. Clinical cases (e.g. discharge summaries, clinical cases published in journals, and clinical cases from medical training resources) provide a very valuable source of information for data-driven technologies aiming at predicting clinical outcomes and patient behaviors. This three-year PhD offers a unique context of a collaboration between FBK, specifically the NLP group, and a Swiss multinational healthcare company, worldwide leader in biomedical research. The research will focus on a number of application oriented tasks, including automatic recognition of clinical entities (e.g. pathologies, symptoms, procedures, and body parts, according to standard clinical taxonomies such as ICD-9, ICD-10 and SNOMED-CT); detection of temporal information (i.e. events, time expressions and temporal relations, according to the THYME TimeML standard), and factuality information (e.g. event factuality values, assessment of the effect of negation, uncertainty and hedge expressions). Italian will be the major language of clinical cases, although technologies will be experimented on other languages. The goal of the PhD is both to advance the state of the art for clinical case analysis for the Italian language, and to deliver prototype applications, which can be further made operative in real settings (e.g. hospitals). The candidate will have the unique opportunity to explore different domains (Natural Language Processing, Machine Learning, Health & Well-Being) being directly coached by very experienced teammates. The involved PhD will work in an international environment, collaborating with a healthcare company, with worldwide presence. The candidate will work both at FBK (Trento) and at the abovementioned company’s premises (both in Italy and abroad).

Required/Preferred Candidate Skills and Competencies: the candidate should possess basic knowledge on Natural Language Processing and Machine Learning techniques (particularly deep learning architectures). Experience on biomedical data will be a plus. Basic programming skills (e.g. Python) would complete the profile. Proficiency in English is required, basic knowledge of Italian preferable.

Contact: magnini [at] fbk.eu (Bernardo Magnini) (FBK), alessandro.la_torraca [at] roche.com (Alessandro La Torraca) (Roche S.p.A.)

FBK, in collaboration with UFI Innovation Center S.r.l.​- 1 scholarship

E - Development of a novel membrane based on anionic exchange for use in the electrolysis process

AEM (Anionic Exchange Membrane) technology represents one of the most advanced and promising technology for the low temperature electrolysis process for the production of green hydrogen. The most important advantage is given by the low cost, if compared with other similar technologies, like the PEM based electrolysis. The cost reduction is given mainly by the adoption of non-precious-metal compounds in the catalytic layers. Currently the AEM technology is not fully mature for large commercial applications, due to the gap of the AEM durability and efficiency, compared to the other, mature technologies. The main target of this research activity will be focused on the development of advanced deposition technologies to improve the AEM performances in terms of efficiency and durability. More in particular, the research activity will be focused on electrospinning coatings of catalytic nanofibers and on spray coatings technologies. Dry synthesis technologies based on the use of plasma and chemical-plasma hybrid processes will be applied too, as well as sophisticated surface analysis techniques. Physical vapor deposition, surface plasma functionalization and atomic layer deposition techniques will be employed for the membrane surface and bulk properties modification following as much as possible on-pot and scalable methods. X-ray photoelectron and Auger electron spectroscopies will be used to investigate the chemical properties of the membrane’s surfaces related to the searched functionalities, along with the physical properties and mechanical stability of the membranes which will be studied by means of electrical, mechanical and stress measurements. AEM will be characterized about V-I behaviour and impedance measurements to investigate limiting transfer mechanism and main barriers. The final target and expected outcome will also include a preliminary analysis of the scale up of the coating technology to a mass production level, for commercial electrolyzer applications.

Required/Preferred Candidate Skills and Competencies:

  • Catalytic materials
  • Advanced nanomaterials for energy applications
  • Deposition techniques for nanostructured layers and thin films
  • Electrolysis technologies

Contact: laidani [at] fbk.eu (Nadhira Laidani) (FBK), Gianluca.gamba [at] it.ufifilters.com (Gamba Gian Luca) (UFI Innovation Center S.r.l.​)

FBK, in collaboration with Enphos S.r.l.- 1 scholarship

F - Study of Anion exchange membrane electrolyzers: improvements of the performance with the use of innovative functional materials

Among the low temperature electrolysis processes, two main approaches are extensively documented: alkaline water electrolysis (AWE) and proton exchange membrane electrolysis cell (PEMWE). AWE is a well-established and durable technology yet with many shortcomings being a large footprint, difficulties in handling the liquid alkaline electrolyte, and insufficient response time. Anion exchange membrane water electrolysis (AEMWE) can potentially combine the beneficial features of the PEMWE and AWE technologies, low cost, raw materials that do not raise concerns in terms of supply bottlenecks, electrodes that do not include platinum group metals (PGM), stainless steel porous transport layers (PTL) and bipolar plates (BPP), a compact design, the adoption of feeds based on noncorrosive liquids (low concentration alkali or pure water), and differential pressure operation. However, as of today AEMWE is limited by AEMs exhibiting an insufficient ionic conductivity as well as a poor chemical and thermal stability. The thesis will focus onto the development and testing of innovative cell layout and materials and in parallel on the improvement/optimization of existing AEM WEL cells. A second aspect of the work is the development of a novel concept stack AEMWE, based different geometry of flow field and electrolyte distribution to extend the dynamic range of operation as well as the reduction of gas cross over to achieve an high purity hydrogen output. The key concept is to reduce the voltage and increase the current density. The approach of the work will articulate around the following aspects: design of high surface area cell, developing suitable support material for this approach which needs to be active towards water dissociation, selection of the more performant components and finally validation of the AEM-WEL stack layut best candidate.

Required/Preferred Candidate Skills and Competencies: consistent, diligent, independent, innovative, good command of English. Knowledge of Italian is strongly preferred.

Contact: laidani [at] fbk.eu (Nadhira Laidani) (FBK), luigi.migliorini [at] enphos.com (Luigi Migliorini) (Enphos S.r.l.)

BLM Group-Adige SpA - 1 scholarship

G - Ontology-driven support to fault diagnosis of manufacturing machines based on service mail flows and repair records

This research work is the result of a joint investment of ISTC-CNR Laboratory for Applied Ontology and Adige SpA. Adige is a company of BLMGroup, an Italian leading industrial group producing high-end machines devoted to work on metallic pipes and profiles, by laser-cutting, sawing and bending. Adige is located in the province of Trento, the sister company AdigeSYS is also located in the province of Trento, and the headquarter of the group, BLM, is located in Lombardy. Working together with the company's maintenance experts, the PhD candidate is expected to do an ontological analysis of the maintenance process, focusing on the diagnosis of technical malfunctioning, and leveraging on knowledge to be extracted from service mail flows and repair records. The work will include the development of a repair and diagnosis ontology in the specific domain, and an analysis of the ways such ontology, properly integrated within the broader service management system, may reduce the costs and increase the quality of the diagnosis and repair process. On the scientific side, the candidate will learn how to model industrial scenarios (with a focus on maintenance) using rigorous ontological methodologies, working side by side with mechanical engineers and ontology experts.

Required/Preferred Candidate Skills and Competencies: Master in mechatronics, mechanical engineering, management/logistics engineering, computer science, mathematics. A B2 to C1 level in Italian Language is requested, the initial activities will be analyzing italian texts and working with italian collaborators. Preference will be assigned to the candidate with a previous experience both in studying knowledge databases, and in industrial maintenance processes on tooling machineries.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student and, in particular, the intellectual property relative to any developed products and/or services that the Company may market is owned by the Company.

Contact: stefano.borgo [at] cnr.it (Stefano Borgo) (CNR), paolo.galvagnini [at] blmgroup.it (Paolo Galvagnini )(BLM Group - Adige S.p.A.)

FBK, in collaboration with SNAM S.p.A. - 1 scholarship

H - Investigation of the direct ammonia synthesis and its utilization in reversible HT cells

Hydrogen is the most promising among the potential green gases, an essential energy carrier to enable a deep decarbonization, for the sectors difficult to abate, such as heavy industry and heavy mobility. Hydrogen indeed must be extracted by water through electrolysis or other materials of biological origin and wastes. One of the most urgent needs to solve is the scaling up of the sector involving several solutions in the way hydrogen is stored, moved, transported in between production and utilization. Beyond compression, one promising direction for some sectors is that of energy carriers, such as liquid hydrogen and liquid organic hydrogen carriers. Among these, ammonia. For its specific characteristics, ammonia could be an ideal carrier in terms of physical and chemical properties, energy density, enabling an efficient logistic and an ideal use in the hydrogen chain. One of the gaps is its synthesis and its direct utilization. This is potentially feasible through innovative technologies, such as reversible Solid Oxide Cells. The PhD will focus on these two dimensions to enable a safe and efficient generation of ammonia in (co)-electrolysis processes through Solid-State Ammonia Synthesis (SSAS) and its utilization in Direct Ammonia Fuel Cells The PhD will focus on both modelling and engineering as well as on experimental and validation activities for cell and short stack based Solid State technology. The activities will include: • Preliminary study on enabling key technologies for the ammonia synthesis; • Engineering study for direct ammonia synthesis, to design and develop both the single components level and the overall integrated system in terms of sizing, Balance of Plant, integration layout and controls; • Demonstration on lab scale of ammonia synthesis through SSAS process and its utilization in Direct Ammonia Fuel Cells based on Solid oxide and/or Proton Conductive Ceramic technologies.

Required/Preferred Candidate Skills and Competencies:

  • Competences in energy engineering
  • Knowledge of the Hydrogen chain
  • Knowledge on conversion processes using both Electrolyser and fuel cells
  • Lab training in use of hydrogen related compounds, including hydrogen carriers

Contact: testi [at] fbk.eu (Matteo Testi) (FBK), alessio.gambato [at] snam.it (Alessio Gambato) (SNAM S.p.A.)

FBK, in collaboration with MindEarth - 1 scholarship

I - Deep Learning for Understanding Visual Scenes additional (published on 27/07/2021)

This PhD project is focused on using deep learning approaches, computer vision and photogrammetry for understanding visual scenes, in particular related to urban environments and people’s behaviours. The project aims at designing novel deep neural network architectures able to exploit multiple sources of data efficiently and to detect people's behaviours, objects, vehicles, in crowded environments such as streets, squares, malls, etc. For example, the project might involve street-view imagery in conjunction with satellite imagery, 3D data, etc. to predict urban outcomes and people’s behaviour. In addition, the candidate will work on generative models (e.g. Generative Adversarial Networks) to augment training data on urban and behavioral patterns (e.g. people movements), which have to be realistic and capture the high diversity of urban forms and lifestyles observed across the globe. Special emphasis will be given to exploit synthetically generated data within GAN architectures. The ideal candidate is strongly motivated to develop machine learning skills focusing on deep learning computer vision and multi-modal approaches. The project will be supervised by Bruno Lepri (FBK), Nicu Sebe (DISI), and Emanuele Strano (MindEarth).

Required/Preferred Candidate Skills and Competencies: Background in computer vision with knowledge on object recognition, image segmentation, deep learning techniques. Interest for applications to visual scene understanding (in particular, urban environments).

Contact: lepri [at] fbk.eu (Bruno Lepri) (FBK), emanuele.strano [at] mindearth.org (Emanuele Strano) (MindEarth) 

FBK, in collaboration with MindEarth - 1 scholarship

J - Using Satellite Imagery and Deep Learning for Understanding Socio-Economic Development additional (published on 27/07/2021)

Some recent works have shown that the combination of high-resolution satellite imagery and machine learning/deep learning techniques have proven useful for a range of socio-economic and sustainability-related tasks, from poverty prediction to population mapping, from forest and water quality monitoring to the mapping of informal economic activities and settlements, etc. This PhD project aims at designing novel deep learning approaches and novel ways for combining satellite imagery collected at different temporal and spatial resolutions, combining different types of data (for example, optical + radar), and/or combining satellite imagery with other relevant data such as information captured by mobile phones. Moreover, special focus will be dedicated to address applications characterized by the limited amount of reference training information (e.g., property valuation, spatial wealth distribution, exposure to respiratory diseases, etc.).

The ideal candidate is strongly motivated to develop machine learning and remote sensing skills focusing on deep learning, satellite imagery and multi-modal approaches, as well as interested in applications to socio-economic and sustainability challenges. The project will be supervised by Bruno Lepri (FBK), Emanuele Strano and Mattia Marconcini (MindEarth).

Required/Preferred Candidate Skills and Competencies:Background in computer vision with knowledge on object recognition, image segmentation, deep learning techniques. Interest for applications to visual scene understanding (in particular, urban environments).

Contact: lepri [at] fbk.eu (Bruno Lepri) (FBK), mattia.marconcini [at] mindearth.org (Mattia Marconcini) (MindEarth), emanuele.strano [at] mindearth.org (Emanuele Strano) (MindEarth)

Athonet S.r.l. - 1 scholarship

K - 3GPP Edge Observability and Scalability additional (published on 30/07/2021)

The Athonet Connectivity Platform (ACP) Edge is a customer-premises equipment (CPE) which runs the user-plane network functions of a Long-Term Evolution (LTE) network, while the control-plane network functions are shared and deployed on the public cloud. The ACP Edge enables access to private LTE scenarios and use cases, keeping customer’s traffic local just like a Wi-Fi access point but exploiting the advantages of the LTE radio access technology. It is important to keep monitored the status of virtual network function (VNF) key performance indicators (KPIs) using modern aggregation system such as Prometheus. At the same time, the KPI collection system must be able to scale following the network load without increasing the cost of the PaaS solution. Various implementations will be evaluated to identify the better scalable and cost-effective solution, evaluating various Prometheus-like implementations and flavors. In addition to KPIs, another tool used to troubleshoot network also by the end user is a log collection system. The existing implementation needs to be improved to increase the usability of the system, from the customer’s point of view. Several solution will be evaluated and implemented. Following observability of the Edge VNF, some improvements may be introduced on the Edge Platform itself: starting from a single Edge Platform, internal optimization can be studied towards Edge Horizontal Scalability. The local Edge System, hosting the Edge VNF, can be further optimized in the IP networking integration: the Edge-to-Cloud connectivity can be reimplemented using standard secure protocols like IPSec; the internal networking stack can be expanded to use Hardware Offloading capabilities. At the same time, the system should be able to use multiple edge nodes at the same location at the same time, being able to balance the load among available systems: this will require to study and implement a local control layer which will orchestrate available local resources. The local scalability requires also a common method to assign in predictable and common method IP Addresses and resources to mobile users connected to the Edge: this will be possible by studying and implementing an AAA layer (3GPP-standard RADIUS) integrated between the Edge and the Cloud. Following the Edge optimization and observability, it is as well important to implement scalable and zero-downtime software upgrades on central cloud control plane. Using 3GPP standard protocols and interfaces, a method to deploy and upgrade 3GPP NF will be studied to minimize service downtime for the user, using for example a Blue/Green methodology. End-to-end testing of new features and implementation is fundamental to assess stability and resiliency of the whole system. On-cloud and on-premise automatic testing and deployment systems will be studied, evaluating several solutions and implementation, to reach and easy and light verification system. Following new coming standards and “de facto” utilization, 5G SA NFV will be integrated in the Edge and Cloud solution, allowing the end user to exploit advantages of high speed and low latency characteristics of the 5G SA solution.

Required/Preferred Candidate Skills and Competencies: This doctoral thesis calls for an exceptionally motivated candidate, with both a research- and an implementation-oriented profile. A M.Sc. degree in Telecommunications Engineering or in Computer Science with a background on mobile wireless networks and virtualization is mandatory. Good knowledge of Linux and its networking stack, as well as some basic scripting capabilities is preferable.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.

Contact: paolo.casari [at] unitn.it (Paolo Casari) (UniTrento), Federico Favaro (Athonet S.r.l.)

COSMAN S.r.l. - 1 PhD Executive

AE - Machine Learning and artificial Intelligence techniques applied to Up-to-date procurement and supply-chain methodologies for Digital Cost Management cost management

COSMAN is a leading company in carrying out cost management projects in Italy, optimizing business costs, improving efficiency and maximizing results of medium and large organizations. As the quantity, variety, and velocity of data available through digital means continues to grow, the ability to analyze and exploit it for cost management purposes becomes essential. For this reason, as from 2016 Cosman developed an innovative ‘Digital Cost Management’ approach to cost analysis and as from 2019 started developing SHAPER®, a proprietary software platform that allows a constant tracking and management of costs. Nevertheless, such a tool is still based mainly on ‘traditional’ analytic and specialistic approaches and does not take full advantage of the new opportunities offered by the growing availability of a large quantity of digital data and of ML and AI algorithms and techniques. At the same time, the academic studies focusing on the integration of innovative analytical approaches in cost management and accounting are still scarce. There is the need to develop a better understanding of the adoption of innovative approaches and tools based on ML and AI in cost management, and their organizational implications as well, especially through the development and validation of specific solutions. In line with these managerial and theoretical objectives, the PhD proposal consists in exploring the possibilities of using ML and AI algorithms in the world of cost management to improve efficiency and effectiveness of COSMAN’s optimization services. The main motivation relies on the fact that an enormous increase in the amount of data about costs and their generation processes, made available by the digital transformation, allows for a finer-grained and therefore more effective analysis, capable of highlighting details otherwise impossible to detect with traditional analysis. In particular, the research aims at finding new approaches, both from a managerial/methodological and a digital point of views, for searching and improving cost reduction strategies by building a framework that can manage different cost categories as well as solving specific domain problems. This will imply investigation and analysis of up-to-date procurement and supply-chain methodologies as well as ML/AI techniques applied to cost management. The final aim will be to develop and test prototype tools that could be further developed and integrated into the SHAPER® platform. The candidate will have the unique opportunity to explore different cost management domains (human resources, energy, telecommunication, sustainability, logistics, materials, etc.) in partnership with experienced professionals. The PhD candidate will develop a strong background in machine learning and statistics, as well as very practical engineering skills to handle data. The close collaboration with COSMAN and the integration of novel analytical approaches in a real organizational setting - will provide a very deep understanding of the major application case studies.

Required/Preferred Candidate Skills and Competencies: 

  • Master’s degree in Computer Science or Software engineering
  • Basic knowledge on Data Modeling, Symbolic and Machine Learning and AI techniques
  • Strong Data Analysis skills
  • Experience in Cost Management and Cost Optimization
  • Experience working as data and sw analyst/manager in at least two or more of the following fields: Hr, Energy, Telecommunication, Transport, Cleaning and Maintenance

The intellectual property of the research results that will derive from the activities carried out by the doctoral student and, in particular, the intellectual property relative to any developed products and/or services that the Company may market is owned by the Company.

Contact: paolo.giorgini [at] unitn.it (Paolo Giorgini) (UniTrento), Giorgio Dossi (COSMAN S.r.l)

MAVTech S.r.l. - 1 PhD Executive

BE - Development of innovative digi-tech solutions for smart farming

The project will be characterized by the objective of creating an automatic real-time and remote monitoring system for crop disease detection. The proposed solution will facilitate the production of organic apples, substantially reducing both the costs of crop inspections and the use of pesticides with Precision Farming techniques. In particular, the proposed system consists of two subsystems: photo-traps installed in the field for the acquisition of the daily health status in real-time of a significant part of the crop, but also from an Unmanned Aerial Vehicle (UAV) for sporadic data collection to perform a complete analysis of the entire crop. The innovativeness of the proposed system derives from the integration of two technologies, one of which is used for the detection of a potential stress state of the crop, while the other provides an overview of its health status.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.

Contact: farid.melgani [at] unitn.it (Farid Melgani) (UniTrento), Gianluca Ristorto (MAVTech S.r.l.)

SoftJam S.p.A. - 1 PhD Executive

CE - Interpretation of very large-scale conversational data

In recent years there has been a growing interest in conversational AI, and a number of conversational systems are now operative in various sectors, including call centres. This situation has made available a huge amount of user-machine interactions, which have a high potential to be used to improve the system performance. As an example, the capacity to detect the emotional content of the conversation would allow the system to respond in a more appropriate way to the user requests. This PhD grant addresses some of the scientific challenges which are behind the interpretation of very large-scale conversational data, including managing noisy data, topic clustering, semi-supervised intent classification, and emotion detection. The resultant of these techniques will be applied to develop empathic chatbots able to model their answers, in real time, on the basis of the human detected emotions expressed during the conversations.

Required/Preferred Candidate Skills and Competencies: Required: Master’s Degree in Science, Computing & Technology, Statistics, Engineering or Mathematics Preferred: Documented experiences in the use Machine Learning techniques applied to real data.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.

Contact: lavelli [at] fbk.eu (Alberto Lavelli) (FBK), l.gemme [at] softjam.it (Laura Gemme) (SoftJam S.p.A.)

Huawei Technology Duesseldorf GbmH - 1 PhD Executive

DE - IoT and Cloud Data security including: (1) Situation Aware, Dynamic Authorization Automotive, (2) Data-centric usage control policies for sovereign data exchange, (3) Bridging federated and decentralized identity - additional (published on 08/07/2021)

PhD Candidates for Digital/Data Sovereignty technology research in Cloud, IoT and IoV
Research topic: Research and develop new solutions for Data Protection, Resilience and Accountability, focusing on one (or a combination of) the following areas: Data usage control in federated data spaces and cloud platforms, decentralization of identity in federated data clouds, innovative models and technologies for IAM in automotive and Fusion of Bayesian Networks / Deep Learning with subjective trust networks and dynamic authorization.

The candidate has the opportunity to work on real world problems and improve data protection and accountability of Huawei future’s products and services. The candidate will have the opportunity of involvement in international initiatives enabling data and identity sovereignty including for example federated data spaces, GAIA-X and ESSIF. More details.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.

Contact: bruno.crispo [at] unitn.it (Bruno Crispo) (UniTrento), theo.dimitrakos [at] huawei.com (Theo Dimitrakos) (Huawei Technologies Dusseldorf GmbH)

ETC Sustainable Solutions S.r.l. - 1 PhD Executive

EE - Machine Learning Methods for Wastewater Plant Monitoring additional (published on 14/07/2021)

The PhD proposal is a project linked to two worlds: green economy and machine learning. The aim is to optimize water treatment with the collection, validation and analysis of data from treatment plants, generating predictive models and promoting development based on sustainability, efficiency and circularity. The PhD candidate will investigate anomaly-detection algorithms for wastewater plants, with a particular focus on analysis of data consistency and on creation of soft sensors, combining process expert-driven knowledge and data-driven models. The objective is to develop intelligent process automation software, moving from reactive solutions to predictive models. The impact of this project is social, related to environmental improvement of rivers, sea and lakes, financial, due to direct reduction of energy consumption and environmental, improving the quality of water at national and potentially international level within developed countries. The PhD candidate will develop a strong background in machine learning and statistics, as well as very practical engineering skills to handle data. The strict collaboration with ETC, will provide a very deep understanding of the major application case study, i.e. optimization of water and wastewater treatment plants.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.

Contact: farid.melgani [at] unitn.it (Farid Melgani) (UniTrento), lorenzo.rizzoli [at] etc-eng.it (Lorenzo Rizzoli) (ETC Sustainable Solutions S.r.l.)

Iveco Defence Vehicles S.p.A.. - 1 PhD Executive

FE - Electronic architecture and applications for enhanced vehicle control additional (published on 29/07/2021)

The target of this activity is the design of a control architecture (called as LLU) able to extend the capabilities of a typical automotive architecture by integrating additional sensors required to control vehicle dynamics and smart actuators required to robotize vehicle commands. In addition, it will be integrated an interface usable by a high-level control system (called as HLU) which could act as communication channel in case of unmanned platforms (tele-controlled) or a mission electronic control system. In general, both traditional vehicle architectures and advanced architectures will be considered. Traditional ones will require the integration of smart actuators over typical driver command (i.e. steering wheel, brake pedal, accelerator pedal). Advanced ones will be based on drive-by-wire commercial components. Moreover, it is desirable to evaluate the functional integration between the LLU and the last generation of GSR systems. The research activity will analyze the more suitable technologies to implement the LLU considering different perspectives: The Hardware (considering commercial board) and the software. The goal of the software analysis will be to understand and realize a framework/toolchain which allow to integrate both the components and the LLU functional simulation. During the activity will be realized a prototype to develop, debug, simulate and evaluate the performance by using HIL (Hardware-In-The-Loop) techniques.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.

Contact: roberto.passerone [at] unitn.it (Roberto Passerone) (UniTrento), rosario.merlino [at] cnhind.com (Rosario Merlino) (Iveco Defence Vehicles S.p.A.)

Arcoda S.r.l. - 1 PhD Executive

GE - 3D Reconstruction and optimization of indoor environments: the creation of accurate digital twins for the exploration on Extended reality devices additional (published on 17/08/2021)

The project aims at deepening the knowledge and improving the state of the art in the domain of 3D data analysis, with particular attention to the mapping of indoor environments. The study of the state of the art will provide the candidate with sufficient background to envisage innovative solutions based on machine learning and deep learning, with the goal of overcoming the limitations of existing methods. It is known in the literature that visual artifacts are often visible, due to imprecisions and reconstruction errors. Such errors are on the one hand related to the computation of the camera pose (intrinsic and extrinsic parameters), and on the other hand, can be traced back to the environmental configuration. This is known as the so-called aperture problem causing mismatches while aligning the anchor points extracted by the cameras. To make the acquisition process more intuitive, an additional objective is to relax the constraints during the acquisition phase, letting the user capture the pictures according to his/her personal needs, rather than following a pre-defined path.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.

Contact: nicola.conci [at] unitn.it (Nicola Conci) (UniTrento), Daniele Sevegnani (Arcoda S.r.l.)

CoRehab S.r.l. - 1 PhD Executive

HE - Implementing rehabilitation tools of REPAIRS in commercial rehabilitation video games additional (published on 19/08/2021)

In the context of the ETN REPAIRS program, the candidate will be responsible for creating a new module of Riablo, i.e., CoRehab commercial solution for in-clinic rehabilitation of patients based on non-immersive virtual reality exergame approach. The module shall integrate the scientific findings investigated by the candidate to advance the state-of-the-art motor rehabilitation of the upper limb following the perception-action approach from a system perspective. With Riablo, patients' movements are tracked through IMU sensors that let patients interact in real-time with the virtual scene to accomplish several motor tasks. Riablo offers different modules that, on the one hand, let the therapists evaluate patients' functional abilities with specific clinically evaluated tests and, on the other hand, offer a variety of exergames to improve patients' motor capacities. The candidate will also explore novel methodologies to advance the field of rehabilitation and consequently to augment Riablo functionalities such as:

  •  An automatic evaluation based on an AI algorithm to suggest a patient-specific rehabilitation path to the therapist, based on the clinical condition and the history of the patient;
  • Exergames for the upper limb rehabilitation of neurological patients;
  • New and innovative concurrent and delayed sensory feedback systems to optimize rehabilitation efficacy;
  • Novel metrics to quantify and monitor patients' improvements in motor functional abilities.

As part of the Ph.D. course in Industrial Innovation, the module and methods will be tested and clinically validated to be commercialized as part of our Riablo product. The involved Ph.D. will work in an international and interdisciplinary environment, thanks to the collaboration between CoRehab, FBK, and the ETN REPAIRS, where she/he will be connected with International academic, clinical, and technology experts and with REPAIRS Ph.D. students.

Required/Preferred Candidate Skills and Competencies:

  • M.Sc. in Biomedical Engineering (Biomechanical background is preferred);
  • Intermediate or advanced knowledge in Python and C# programming languages;
  • Experience in using game engines (previous experience in using Unity 3D is considered as a plus);
  • Previous experience in using Motion Capture Analysis systems and IMUs sensors for human movement analysis is required.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.

Contact: efarella [at] fbk.eu (Elisabetta Farella) (FBK), stefano [at] corehab.com (Stefano Lenzi) (CoRehab S.r.l.)

Resonanz Capital GmbH - 1 PhD Executive

IE - Portfolio Selection and Machine Learning additional (published on 19/08/2021)

The modern portfolio theory (MPT) introduced by Harry Markovitz in1952 opened a new era for portfolio analysis. Instead of single-minded pursuit of high returns or a naïve approach for portfolio diversification, the MPT helps investors to find a balance between high returns and low risk. Assuming that asset returns are normally distributed, the MPT requires only two input estimates: the expected returns and the expected covariance matrix of returns. By solving the optimization problem, the investor can find the optimal asset allocation with the lowest expected risk for a given expected return.

The meanwhile almost 70-years old approach has been challenged through different aspects, especially regarding its instability in expected returns and covariance matrix. The output can vary dramatically with small changes in assumed expected returns and covariance matrix. The reason lays in the inversion of the covariance matrix, which is essential in mean-variance optimization. In some extreme scenes, classical MPT could even underperform the naïve equal-weighted portfolios.

A new challenge comes from the recent development of the finance industry, as the number of available alphas is growing exponentially. Here, the alpha could be any financial instruments or strategies with a reasonable return that one may wish to trade on. However, these alphas are even fainter and vary with time. To construct a tradable portfolio with a small number of active positions, one needs to analyze an even bigger portfolio with a huge number of alphas. The optimization problem becomes underdetermined as the total number of variables (weights of alphas) exceeds the number of observations (data available). The covariance matrix becomes singular and the inverse of it does not exist.

The objective of this research is to find a robust numerical method to solve the problems mentioned above. The new method should be able to construct optimal portfolios with good out-of-sample risk and return performance properties. It should also be computationally efficient to be able to catch short-term alphas. In the final step, the new method will be applied to nontraditional data sets with a huge number of alphas to test its effectiveness.

Required/Preferred Candidate Skills and Competencies: Background in mathematical finance with knowledge on machine learning methods and operations research. Strong data analysis skills and programming skills.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.

Contact: sandra.paterlini [at] unitn.it (Sandra Paterlini) (UniTrento), vincent.weber [at] resonanzcapital.com (Vincent Weber) (Resonanz Capital GmbH)

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