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.

38th Cycle - Intake year 2022

Leonardo S.p.A. - 6 scholarships

A, B - Quantum Technologies for Imaging & Communications

The proposed research activity concerns the study of quantum sensing and quantum communication protocols for the detection of distant objects or signals in the presence of traditional background noise. As to the quantum sensing, solutions such as Non Line of Sight Imaging, or Quantum Ghost Imaging, will be investigated and experimentally verified considering realistic contexts, e.g. addressing issues as presence of background noises, non-negligible clutter, low signal levels, etc. Other approaches in terms of either sensing or communications techniques may arise from a preliminary analysis and be identified as a target of the study.

Contact: lorenzo.pavesi [at] (Lorenzo Pavesi) (UniTrento), massimiliano.dispenza [at] (Massimiliano Dispenza) (Leonardo S.p.A.)

C - Characterization and modeling of graphene-enhanced polymer composites for high-performance structural applications

SGraphene, an atomically thin honeycomb carbon lattice, has emerged as swiss knife of advanced materials with a broad combination of properties. Its electrical, thermal, optical and mechanical properties are exceptional. The bulk density of graphene is another prominent character to make it suitable for high-performance composites. Nevertheless, graphene and graphene-based 2D materials (for example graphene oxide) are hardly adopted for high-performance structural composites such as fiber-reinforced plastics (FRPs) for aerospace sectors. In this project, graphene and graphene-based 2D materials will be studied, classified and embedded in the current FRP systems to enhance their static and dynamic mechanical properties. In particular, the graphene-enhanced FRP systems will be prepared, characterized and modeled to understand their mechanical enhancement mechanisms. The main focus of the study will be to develop a mathematical model to understand the effect of graphene morphology (lateral size and thickness) on the mechanical properties of FRP systems. In addition to that, innovative sustainable processes will be developed to incorporate the nanofillers into the composite materials with the potential to scaling-up.

Contact: nicola.pugno [at] (Nicola Pugno) (UniTrento), muhammad.zahid.ext [at] (Muhammad Zahid) (Leonardo S.p.A.)

D - New Accelerated Computing Technologies for Model Base Digital Twins and AI Frameworks

Topics of interests, but it is not limited to, spawn from Scientific Computing to scalable Deep Learning on modern parallel computing systems.

High Performance Data Analytics:

  • Design emerging parallel and scalable data-analytics algorithms on GPU / MultiGPU systems (this may include algorithms for DNN/GNN, graph analytics).
  • Integration into existing open source frameworks (e.g, NVIDIA RAPIDS, Apache Arrow, BlazingSQL).
  • Design and performance analysis of parallel and scalable methods for interactive BI dashboards on GPUs and integration into modern open source alternatives such as Plotly Dash.

Scientific Computing and AI convergence.

Physics Informed Neural Networks (PINN) as an alternative to standard CFD solvers to take advantage of GPU accelerated machine learning frameworks. Topics and activities of interest for the PhD candidate might be:

  • Prototyping PINNs, experimenting with different network architectures, training algorithms and open source frameworks such as PyTorch, Tensorflow and Modulus.
  • Contributing to the development of internal company libraries and eventually contributing and/or customizing the above mentioned open source frameworks.
  • Integrating PINNs with standard MBSE 0D/1D simulation tools such as AMESim and Simulink to replace and/or standard reduced order models. Scientific Computing
  • Analysis and acceleration of Digital Twin applications on multi-node, multi-gpu heterogeneous HPC systems, with particular focus on performance modeling, portability e scalability aspects, for lattice-based methods of industrial interest (e.g., PIC - Particle-in-Cell techniques, Lattice-Boltzmann algorithms);
  • investigation of parallel and scalable primitives and performance portable programming models for Scientific Computing (e.g., BLAS libraries).

During the PhD, the candidate will have the opportunity to: integrate his/her research outcomes into the Leonardo Labs technology infrastructure in collaboration with Leonardo researches, visit Leonardo labs periodically, play with high-end systems (e.g., DaVinci-1) and parallel architectures equipped with GPU Accelerators, attend top venues conferences (such as IEEE SuperComputing, PPoPP, IPDPS, ISC, ICS, CF), contribute to European Projects where Leonardo Labs and UNITN are involved.

Contact: flavio.vella [at] (Flavio Vella) (UniTrento), antonio.sciarappa.ext [at] (Antonio Sciarappa) (Leonardo S.p.A.)

E - Domain adaptation from generated data to real domain

Deep learning methods have been successfully applied to different visual recognition tasks, demonstrating an excellent generalization ability. However, analogously to other statistical machine learning techniques, deep networks also suffer from the problem of domain shift, which is observed when predictors trained on a dataset do not perform well when applied to novel domains. Since collecting annotated training data from every possible domain is expensive and sometimes even impossible, over the years several Domain Adaptation methods have been proposed. Domain Adaptation approaches leverage labeled data in a source domain in order to learn an accurate prediction model for a target domain. The project will investigate novel approaches based on deep generative models to perform Domain Adaptation and alleviate the problem of domain shift when data of the source domain are synthetically generated with simulators and target data correspond to images collected in the real world. Approaches which specifically account for geometric 3D information will be devised. The developed techniques will be showcased in relevant computer vision tasks, such as object detection and multiple object tracking.

Contact: e.ricci [at] (Elisa Ricci) (UniTrento), farid.melgani [at] (Farid Melgani) (UniTrento), alessandro.nicolosi [at] (Alessandro Nicolosi) (Leonardo S.p.A.)

F - Frugal NLP for operative context

The main purpose of the thesis is to study the application of NLP techniques developed for low resource languages and dialects for operative contexts characterized by highly specific lexicon/taxonomy/sentence structure The first case study will be the following: starting from a collection of information from Italian closed sources, written in a professional language that refers to a specific taxonomy and concerning security or public order, the goal is twofold:

  • On the one hand, obtain a structured representation for statistical and predictive analysis purposes
  • On the other hand, to create a solution that allows consultation in terms of question answering

The generalizability of the findings will be evaluated on a different “sub-language” (i.e. colloquial speech in an Italian dialect).

Contact: giuseppe.riccardi [at] (Giuseppe Riccardi) (UniTrento), francesco.calabro [at] (Francesco Calabro) (Leonardo S.p.A.)

FBK, in collaboration with Naver Labs Europe - 1 scholarship

G - Unified Foundation models for Speech-to-Speech Translation

Current trends in Artificial Intelligence are characterized by the migration from “task-specific models” to the so-called “foundation models”. The former are usually trained on large datasets labelled for specific target applications. The latter, much more reusable and flexible, are trained on a variety of unlabeled data to perform different tasks with minimal fine-tuning. In recent years, the introduction of such models (e.g. BERT, GPT-3) has opened an unprecedented range of opportunities, with an explosion of AI applications fueled by the power of transfer learning across diverse data and tasks. In line with this trend, this PhD aims at investigating and training deep neural architectures to build large multimodal sequence-to-sequence models able to encode input speech and text in a common space, also being able to decode output text or speech from a common representation. Such a multimodal architecture, if then made multilingual, could become the future unified foundation model for building automatic speech recognition (ASR), text-to-speech synthesis (TTS), speech-to-text translation (S2T) and speech-to-speech translation (S2S) systems from a single backbone. For further information see also:

Required/Preferred Candidate Skills and Competencies: MSc or equivalent degree in computer science, artificial intelligence, computational linguistics, engineering, or a closely related area. In addition, the applicant should:

  • Have interest in Machine and Speech Translation
  • Have experience in deep learning and machine learning, in general
  • Have good programming skills in Python and experience in PyTorch
  • Enjoy working with real-world problems and large data sets
  • Have good knowledge of written and spoken English
  • Enjoy working in a closely collaborating international team

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

Contact: negri [at] (Matteo Negri) (FBK), laurent.besacier [at] (Laurent Besacier) (Naver Labs Europe)

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

H - Artificial Intelligence for Digital Transformation

Digital technologies play an ever increasing role in all aspects of human society; this induces a wide range of changes, collectively referred to as Digital Transformation, that, far from being only technological, also cover cultural, organizational, social, managerial aspects of our life. Artificial Intelligence is a key technology for digital transformation, thanks to its capability to extract information and knowledge from data; this requires the capability to open, analyze and exploit all data available on a given phenomenon, data that are often highly heterogeneous, scattered, and coming from different sources (e.g. open, sensor, free, closed, linked data). This thesis will concentrate on developing a data-driven computational framework, based on AI approaches, able to perform data analysis and prediction in the setting just described. The framework will be developed in the scope of the Digital Hub, a digital platform jointly developed by Dedagroup and Fondazione Bruno Kessler to address digital transformation in different application domains, including Public Administration, Digital Finance, Digital Industry. The validation of the framework will be performed addressing problems in these application domains, by exploiting the data sets and services integrated in the Digital Hub

Contact: raman [at] (Raman Kazhamiakin) (FBK), Roberto Loro (Dedagroup S.p.A.)

SWS Engineering S.p.A. - 1 scholarship

I - Innovative technologies and processes for digital transformation and sustainable development in the large infrastructure sector

The concept of Digital First in the infrastructure sectors reflects the trend towards offering design activities as a digital service and the gradual shift from delivering printed documents to digital contents as a service. According to this somewhat innovative thinking, everything is accessible, connected and digitised via the virtual digital world.

In general, the objectives that the Candidate shall pursue during the PHD are the acquisition of skills and technologies necessary to overcome the barriers that hinder the digital transformation in the infrastructure sector. In details, the Candidate shall investigate integration processes for the lifecycle management & sustainability assessment of infrastructure related works to provide methodologies and tools that in the medium-term allow to:

  • produce digital contents the most suitable for the different project phases, avoiding gaps within the “design and build” process;
  • develop tools to support users to interact with digital content on-site (Extended reality - XR);
  • operate advanced cryptography-based track and trace solutions for the implementation of sustainability certification systems in the construction sector with the support of smart contracts;
  • set up a cloud-based digital engineering service marketplace.

Some topics the candidate will pursue during the scholarship are: Requirements Engineering, Information and knowledge management in organization, 3D real-and-virtual combined environments and human-machine interactions, Environmental Sustainability, Mathematical Methods for Engineering, Decision-Making under Certainty, Risk and Uncertainty.

Contact: roberta.cuel [at] (Roberta Cuel) (Unitrento),  a.danzi [at] (Andrea Danzi) (SWS Engineering S.p.A.)

Solstice Pharmaceuticals B.V. - 1 scholarship

 J - Super-resolution ultrasound imaging using monodisperse ultrasound contrast agents

This PhD position is focused on the development and validation of novel Super-Localization Ultrasound Imaging solutions enabled through dedicated monodisperse Ultrasound Contrast Agents (UCAs). The primary activities (in tight collaboration with Solstice Pharmaceutics) will be the following:

  • characterization of monodisperse UCAs response to ultrasound waves
  • design and fabrication of clinically relevant phantoms
  • development and testing of novel imaging strategies and vessel reconstruction algorithms

Research activities will be conducted both at the Ultrasound Laboratory Trento (Italy) and at Solstice Pharmaceutics (the Netherlands). The selected candidate will thus have the opportunity to experience an international working environment as well as gain academic and industrial experience. Additional allowance is available to support the periods abroad. The preferred candidate has experience in biomedical imaging, signal processing, image formation, image analysis, and machine learning.

Contact: libertario.demi [at] (Libertario Demi) (Unitrento), w.vanhoeve [at] (Wim van Hoeve) (Solstice Pharmaceuticals B.V.)

FBK, in collaboration with Gi Group - 1 additional scholarship

 K - Fair and transparent machine learning models for talent selection

In the last few years, several companies and researchers have designed AI-based approaches for assessing and selecting the best candidates and talents based on allegedly objective performance criteria. Ideally, the usage of machine learning in assessment has the objective of mitigating the biases which often affect the hiring decisions conducted by human recruiters. However, several studies have shown that also machine learning algorithms can be characterized by biases. Hence, the goal of this PhD project is to develop and evaluate innovative, fair and explainable machine learning models for inferring candidates’ employability, performance, and individual characteristics. In particular, the candidate is expected to develop innovative natural language processing and/or multimodal (audio-video) algorithms to extract and to infer information from professional resumes and/or video job interviews in order to evaluate skills, possible future performances and individual characteristics of candidates. Moreover, state-of-the-art and innovative approaches to machine learning fairness will be implemented and evaluated by the student. The outcome of the student may consist in research prototypes that will be tested on Gi-Group data as well as on patents and scientific publications in top-tier conferences (AAAI, ACL, IJCAI, AIES, FaccT, etc.) and journals. The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by FBK and the Company. 

Required: Good expertise in programming, good knowledge of Python
Preferred: Background in machine learning, natural language processing, computer vision

Contact: lepri [at] (Bruno Lepri) (FBK), luca.orlassino [at] (Luca Orlassino) (Gi Group)

FBK, in collaboration with Sony Europe B.V. - 1 additional scholarship

 L - Highly efficient, integrated, parallel digital machine learning architectures for imaging systems

Sensing devices interact in complex environments and their miniaturization and portability call for small form factor and low power consumption. Edge AI is therefore of fundamental importance for robustness, privacy, and long battery operation. The application of AI algorithms to image sensors further constrains technological solutions in resources due to the large amount of generated data. The objective of this project is to study HW-friendly solutions exploiting digital parallel and dedicated HW/SW architectures aimed at solving specific sensing use cases with high efficiency in area and power. The student will have to tackle the challenges with a multidisciplinary point of view, from the study of the sensing problem to the modelling of the solution, and a special focus on the integrated circuit design aspect. Technically speaking, the work will include the development of hardware-friendly algorithms based on deep learning, high-level modeling of an imaging system, simulations of the system behavior in real case scenarios, implementation of the system in a mixed hardware/software platform (which might include PC, SoC, FPGAs, integrated circuits) and characterization of developed system in the lab and on the field. The student will interact with experts in the fields of computer vision and CMOS image sensors gaining a unique combination of background knowledge. The expected outcome is a highly optimized architecture for an imaging system that combines software, firmware and hardware solutions to solve specific, market-driven needs in the consumer electronics, automotive, smart city, robotics, digital industry fields. The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by FBK and the Company.

Required/Preferred Candidate Skills and Competencies: Background in computer vision, digital electronics, or IC design Knowledge of deep learning architectures Knowledge of image sensor architectures Software programming using C++, Python, Matlab Design of digital circuits IC design capability Testing of electronic devices and systems FPGA firmware design

Contact: gasparini [at] (Leonardo Gasparini) (FBK), matteo.perenzoni [at] (Matteo Perenzoni) (Sony Europe B.V.)

FBK, in collaboration with Sony Europe B.V. - 1 additional scholarship

 M - Innovative detectors for THz/IR sensing and imaging systems

THz/IR sensing and imaging systems can expand the capabilities of portable devices beyond the human vision. The objective of this project is to study sensing solutions operating from the THz to the infrared region exploiting antenna-coupled field-effect transistors, including the study and optimization of the devices and the electronic design of integrated readout and control. The student will have to tackle the challenges with a multidisciplinary point of view, from the quasioptical and electromagnetic point of view, the device behaviour in terms of response to an incoming signal and in terms of noise, and a special focus on the integrated circuit design aspect. Technically speaking, the work will include high-level modeling of devices and sensor architectures, with emphasis on sensitivity and propagation of noise through the readout chain, development of novel pixel and readout architectures, circuit design including schematic and layout, and characterization of fabricated devices in the lab. The student will interact with experts in the fields of image sensors, THz detectors, and analog circuit design, gaining a unique combination of background knowledge. The expected outcome is the realization of state-of-the-art image sensors and their validation in a real use-case scenario. The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by FBK and the Company.

Required/Preferred Candidate Skills and Competencies: Background in (analog) electronics Knowledge of image sensor architectures Mixed signal IC design Testing of electronic devices and systems Software programming for the generation of high-level models

Contact: gasparini [at] (Leonardo Gasparini) (FBK), matteo.perenzoni [at] (Matteo Perenzoni) (Sony Europe B.V.)

Huawei Technologies Switzerland AG - 1 additional scholarship

 N - Scalable and parallel tensor algebra programming for irregular applications

The project aims to investigate graph and hypergraph algorithms express in their algebraic matrix/tensor form by considering the temporal aspect that typically occurs in evolving networks. In particular, the Ph.D. candidate will focus on i) the investigation of temporal models for graph/hypergraphs; ii) the identification of a suitable set of algebraic operators and semiring properties to express the temporal model; iii) a methodology for compressing sparse hypergraphs; iv) implementation of operators on parallel and distributed systems.

The Ph.D. student will also have the opportunity to work closer to Huawei researchers, visit the research center in Zurich, and integrate his/her research into Huawei ALP/GraphBLAS.

Contact: flavio.vella [at] (Flavio Vella) (UniTrento), albertjan.yzelman [at] (Albert Jan YZELMAN) (Huawei Technologies Switzerland AG)

Techvisory s.r.l. - 1 additional scholarship

 O - Text mining pipeline for capturing and organizing information from unstructured and heterogeneous text sources, automated semantic analysis and interpretation, with application in various business domains

Contribution to the design, develpment and test of a Deep Learning pipeline platform for the structural and semantic analysis of unstructured and heterogeneous textual sources, for the identification of objects and subjects and their attributes and subsequent use for the estimation of different types of risk, such as for the management of legal litigation, privacy and compliance, customer care processes. It includes the semantic analysis of textual documents for disambiguation of homonyms and identification of relational context with other subjects. Funding company: Techvisory is an innovative startup founded in Trento in 2020 by Franco Bernabè Group - Fb Group - and DeltaInformatica. The company is led by top managers with international experience in technical leadership of large companies and its mission is to support companies to be leaders in innovation and digital transition through the strategic use of data and cloud-ready architectures.

Contact: fausto.giunchiglia [at] (Fausto Giunchiglia) (UniTrento), Paolo.bucci [at] (Paolo Bucci) (Techvisory s.r.l.)

Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI) - 1 PhD Executive

AE - Hybrid approaches for multiscale mathematical modeling integrated with machine learning in industrial pharmaceutical applications

In the industrial drug R&D setting, a challenging aspect is the need of defining computational prediction tools to support the everyday decision-making process. A promising opportunity consists in implementing such computational tools as hierarchical, multiscale computational models able to consider a wide variety of independent or interacting genetic and non-genetic factors acting at different levels of biology (genetic, molecular, tissue, organ, etc.) which concur to the development of a complex disease and its clinical manifestations. Within the project, the candidate will focus on the development and implementation of novel computational approaches to support mathematical modeling in pharmaceutical industrial applications and extend the capabilities in the field in defining hybrid models providing a systems-level understanding of a complex disease, from its low-level molecular description to the high-level representation of its clinical manifestations. These models will describe the involved biological processes according to different levels of detail and will leverage on different modeling approaches, statistical techniques, and machine learning tools to find the best compromise between data availability and the requirement of providing a comprehensive and accurate biological description. The research work will build on a comprehensive assessment of available modeling, statistical and machine learning methods. The knowledge developed from this preliminary step will be then employed to define innovative modeling pipelines for hybrid modeling by extending and leveraging on the ones already developed internally at COSBI. The methodologies resulting from the project will be then applied to modeling cases related to international collaborations in place within the pharma world, providing the candidate with access to unique data and the opportunity to address issues that can impact decision-making within the industrial drug R&D setting.

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: giovanni.iacca [at] (Giovanni Iacca) (UniTrento), marchetti [at] (Luca Marchetti) (COSBI)

FOS S.p.A. - 1 PhD Executive

BE - Artificial Intelligence and Computer Vision in agro-forestry and environmental applications

The project will consist in the study, analysis, development and prototyping of new techniques, mainly based on computer vision and machine learning for the detection, recognition and counting of insects in special field-based traps. The work will focus on both the sensors to be deployed as well as the entire image processing/analysis flow, taking into account the peculiarities of the target cultivation and the insects threatening their health state and potential yield.

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] (Farid Melgani) (UniTrento), giovanni.giannotta [at] (Giovanni Giannotta) (FOS S.p.A.)

FBK - 1 PhD Executive

CE - Optimisation for a process model applied to a research cleanroom

In industrial realities, management systems have a categoriesed structure that studies the scope of application discriminating between the different production phases, the type of resources and the dedicated operating departments. The implementation of a quality management system integrates the Quality principle into manufacturing activities is an opportunity to guarantee the quality of research results and to improve and gain recognition for the work done in a research laboratory. In the context of a clean room of R&D production, we want analysing some process flows, which have been identified for this purpose, having as its ultimate goal the CR's already active quality system management model optimisation. This thesis work involves the use of various evidence-based and statistical tools for the definition and visualisation of processes, the identification of possible failures or criticalities and the definition of consequent corrective actions. This approach will define a new model for assessing and managing non-conformities, which are already dealt with the current quality system. The novelty introduced is the development of a management system starting from the knowledge of industrial realities certified and the codification of know-how developed in the MNF CR itself. A practical system declination is the proposal of preventive and corrective actions as tools for non-conformities and criticalities handling as highlighted in the monitoring of process activities. The thesis is organised in two phases. The first phase analyses Dry Etching process as the case study chosen for the definition of the model described above. This choice turns out to be sufficiently complex to represent a self-consistent system that we expect to capture the variability of operational parameters like a general model. For this reason, the second phase involves the study of the generalisation of the validity of the elaborated model to a second domain, relating to the field of deposition processes.

Required/Preferred Candidate Skills and Competencies: Required: Master in technical and materials characterisation 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: paolo.bosetti [at] (Paolo Bosetti) (UniTrento), dellanna [at] (Rossana Dell’Anna) (FBK)

Huawei Technologies Duesseldorf GbmH - 3 additional PhD Executive

DE - Security & Trust - Connected, Cooperative, Automated Mobility

The position is connected to a new EU Project starting in September 2022 on Security and Trust in Connected, Cooperative, Automated Mobility (CCAM). The PhD candidate will be funded by the project and PhD topic will be connected directly to the research inside this project. Goal is to complete it in 3 years.

Research Topic:

  • Perform research and develop new solutions for Trust Management in the Next-Generation CCAM technologies.
  • Contribute to new mechanisms for assessing dynamic trust relationship based on Zero Trust and Subjective Logic.
  • Define a trust model and trust reasoning framework based on which involved entities can establish trust for cooperatively executing safety-critical functions.


  • Contribute to the research and development of technologies in the upcoming domain of Connected, Cooperative and Automated Mobility (CCAM)
  • Being involved in international initiatives including industry groups such as 5GAA, Gaia-X, DIF and Horizon Europe research projects.

Required/Preferred Candidate Skills and Competencies:

  • Completed master studies (or equivalent) in computer science, information technology, electrical engineering, or mathematics;
  • Background in probabilistic reasoning and logic or formal methods
  • Exposure and understanding of data protection and security development technologies;
  • Good programming skill;
  • Fluent in English.

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

Important: to be admitted to the PhD Program, both the University and the Company selection processes have to be passed through. The selection process at Huawei includes technical and HR interviews.

Contact: bruno.crispo [at] (Bruno Crispo) (UniTrento), ioannis.krontiris [at] (Ioannis Krontiris) (Huawei Technologies Duesseldorf GbmH)

EE - System security

The PhD candidate will contribute to the research, development, publication and product adoption of system security technologies for future heterogeneous computing architectures targeting AI and compute, working on real world problems for a wide range of Huawei products and services to preserve and prove their trustworthiness through technology, from a unique research position connecting Academia and Industry.


  • Assess system security and platform resilience risks of application scenarios such as heterogeneous computing (e.g. AI, compute, accelerators etc.)
  • Research on innovative concepts and architectures in the field of Trustworthy Computing and runtime defenses against runtime attacks
  • Implement proof of concepts and support technology adoption in products
  • Publish research papers and technical reports at top security conferences and collaborate with academic partners
  • Improve, by design, the end to end system security capabilities of future Huawei products

Required/Preferred Candidate Skills and Competencies:

  • You have recently completed or will soon complete your master studies in computer science, information technology, cybersecurity, electrical engineering, or mathematics
  • You have research experience in system security, applied cryptography, computer architectures, OS design
  • Experience with compilers, enclave engines, TEEs, secure processing architectures and accelerator designs is a plus
  • You have good programming skills in C, Rust, C++, Python, Go and in system programming
  • You have demonstrated affinity for concept design and hands-on validation
  • You have a passion for finding solutions for complex technology issues
  • You have excellent collaboration and communication skills
  • You are self-starting and self-motivating, willing to take initiatives and feel the responsibility for your project

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] (Bruno Crispo) (UniTrento), silviu.vlasceanu [at] (Silviu Vlasceanu) (Huawei Technologies Duesseldorf GbmH)

FE - Digital and Data Sovereignty: secure, controlled, and trustworthy data exchange

Digital sovereignty is a multi-dimensional concept that relates to the ability of an individual, or a state to govern the use of their digital assets accordioning to agreements and regulations. As such, the ideal candidate would preferably have background in some of the following topics: security, data governance, identity protocols, cloud protection, distributed ledgers, and/or usage control. The candidate will participate in Huawei’s internal sovereignty projects, and relevant European communities and projects.

Research Topic:

  • Conduct research in novel security technologies related to digital sovereignty. This can include (but not limited to) data protection architectures, data and usage control, accountability, privacy technologies and identity platforms.
  • Advance the state-of-the-art and practice by contributing new solutions and prototypes in the above domains.
  • Analyze the trustworthiness, and verification of compliance of the proposed solutions.

Required/Preferred Candidate Skills and Competencies:

  • A master degree (or equivalent) in computer science or related fields.
  • Passion about secure software, and systems architecture and engineering. 
  • Ability to combine conceptual thinking with strong technical skills
  • Strong programming skills in any of the popular languages e.g., C, C++, Python, Java, Go…etc.
  • Fluency in English

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] (Bruno Crispo) (UniTrento), Amjad.Ibrahim [at] (Amjad Ibrahim) (Huawei Technologies Duesseldorf GbmH)

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

GE - Cybersecurity validation of security architectures for vehicles for defence and off-road applications

IDV is a company that designs and produces defence vehicles and special vehicles for off-road applications (such as for quarries, or homeland security vehicles).

The company is interested in deepening its knowledge in the Cybersecurity field for a variety of reasons, including:

  • IDV Vehicles need to be homologated as civil vehicles as well. For the 2024 General Safety Regulation, Cybersecurity needs to be included
  • There is an ongoing activity of research and development about unmanned ground vehicles, which revolves around a remotely controlled vehicle, a control station, and a communication channel between the two
  • Extend our knowledge in the commercial field and apply it to logistic and tactical military vehicles

The aim of the proposed PhD research is the acquisition of knowledge about Cybersecurity solutions in the automotive field and their validation, with an emphasis on the state of the art in drive-by-wire applications. An investigation on the most advanced solutions on vehicles with increasing autonomous driving capabilities will also be of interest, so to extend this knowledge to unmanned vehicles.
Therefore, the studies concerning an analysis of the communication channel will be crucial.
The research activity will investigate Cybersecurity problems from different points of view, such as hardware and software, with a particular emphasis on validation techniques to apply to the selected design solution.
Lastly, adapting the applicability of Security Regulations and their design solutions to military vehicles will be of great interest, which would be done by comparing the approaches used in the commercial field.

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] (Bruno Crispo) (UniTrento), davide.compar [at] (Davide Campar) (Iveco Defence Vehicles S.p.A.)

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