Reserved topic scholarships - 2023 | Doctorate Program in Industrial Innovation

Reserved topic scholarships - 2023

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.

39th Cycle - Intake year 2023

FBK, in collaboration with UFI CELL spa - 1 scholarship

A - Development of novel catalyst layers for proton exchange membranes for water electrolysis

The research activity aims to develop novel catalyst layers for proton exchange membranes (PEMs) in water electrolysis. The objective is to enhance the efficiency and durability of PEM electrolyzers, which are key components in hydrogen production systems. The expected outcome of this research is the development of advanced catalyst layers that exhibit improved electrochemical performance and stability. By designing catalyst materials with high activity, selectivity, and durability, researchers aim to enhance the efficiency of the electrolysis process, reduce the energy consumption, and extend the lifespan of the PEM electrolyzers.
These novel catalyst layers are expected to enable more efficient and cost-effective hydrogen production through water electrolysis. By optimizing the catalyst composition, structure, and interaction with the membranes, the research activity seeks to overcome the limitations of existing catalyst layers and contribute to the advancement of sustainable hydrogen production technologies.
In the production process of novel catalyst layers for proton exchange membranes (PEMs) in water electrolysis, various techniques are employed to achieve optimal morphology and composition. These techniques include deposition methods, such as physical vapor deposition (PVD) or electrochemical deposition (ECD), and synthesis methods like sol-gel or wet chemical methods.
The morphology of the catalyst layer plays a crucial role in determining its catalytic activity and stability. The student will focus on controlling factors such as particle size, shape, and distribution within the catalyst layer. These parameters directly influence the accessibility of reactants to the active sites, mass transport properties, and overall electrochemical performance.
In addition to morphology, the choice of support material is critical for the catalyst layer's performance. Support materials provide structural stability and serve as a platform for catalyst deposition. Common support materials include metal oxides, including titanium dioxide or cerium oxide and novel material as silicon carbide, and nitrides (vanadium, et.). The selection of the support material depends on factors such as its electrical conductivity, chemical compatibility with the catalyst and electrolyte (as pH and polarization), and durability under the operating conditions of the PEM electrolyzer. By carefully controlling the productive process and optimizing the morphology and support material, the student aim to develop catalyst layers that exhibit high catalytic activity, stability, and efficient charge transfer.
The outcome of this research has the potential to significantly impact the field of water electrolysis by providing a pathway for the commercialization of PEM electrolyzers with enhanced performance and longevity. The development of novel catalyst layers can accelerate the adoption of hydrogen as a clean energy carrier, facilitating the transition to a more sustainable and low-carbon future.
Required/Preferred Candidate Skills and Competencies:

  • MSc in chemistry, materials science, physics, chemistry engineering or similar.
  • Familiarity in deposition process and surface characterization (XRD, XPS, etc)
  • Knowledge in electrochemical characterization for low temperature cells.
  • Familiarity in low temperature water electrolysis and fuel cell.
  • Familiarity with chemical lab procedures.
  • Good knowledge of written and spoken English.
  • Accuracy, proactivity, and goal orientation.
  • Good communication and relational skills.
  • Ability to write technical reports and scientific papers.

Contact:testi [at] fbk.euMatteo Testi  )(UniTrento),giorgio.ercolano [at] (Giorgio Ercolano )(UFI CELL)

FBK, in collaboration with Sony Europe B.V. - Europe Technology Development Centre - 1 scholarship

B - Highly efficient integrated machine learning parallel digital architectures for the optimization of imaging and sensing IC devices

Sony Semiconductor Solutions Group (a branch of Sony Corporation) is a corporate group that conducts research and development, product planning, design, production, and sales of semiconductor-related products, centering on image sensors in the imaging field such as smartphones and digital cameras, and in the sensing field such as automotive, security, and industry areas. A new R&D centre has been opened in Trento and is offering a PhD scholarship jointly with Fondazione Bruno Kessler. The objective of the collaboration within this scholarship is to study neural network architectures suitable for the integration within next generation advanced CMOS image sensors and optical sensors targeting to address the following challenges: i) fully parallel connection and processing between the top-level pixel array and the bottom layers digital processing blocks, ii) high reuse of on-chip memory and computational resources, iii) low power operation.  
The doctoral candidates will have to tackle the challenges with a multidisciplinary point of view, starting from the design and simulations of ideal neural network architectures implementation, developing modelling of sensor+NN, investigating design trade-offs between computational power and chip integrability. 
The outcome of the research would be the result of a feasibility study and a proposed set of features/ hardware architecture which would allow the start of a development in this field. The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by FBK and Sony.
Required/Preferred Candidate Skills and Competencies:
Good knowledge of machine learning/neural network concepts.
Ability to use machine learning development tools such as PyTorch/TensorFlow to evaluate the
required network complexity.
Ability to correlate software-based analysis to HW requirements.

Contact: eliricci [at] (Elisa Ricci) (FBK), gasparini [at] (Leonardo Gasparini) (FBK), davide.marani [at] (Davide Marani) (Sony)

VALUEBIOTECH SRL1 additional scholarship

C - Augmented imaging for minimal invasive robotic surgery

Valuebiotech (Agrate Brianza – MI) is an innovative SME , that is developing the M.I.L.A.N.O. robot surgical platform, an innovative surgical robot for minimal invasive abdominal and thoracic video assisted procedures. 

"Painless, scarless surgery for everyone" is the primary mission of the company. To achieve this goal the company is realizing a platform consisting of several products to meet the needs of fully robotic surgery and also more recent roboscopic techniques. 

The product development of the company is oriented towards the surgical robotic market and in particular to meet the need to improve the perception of the space in which the robot operates as well as provide more information
on the anatomical structures not perceptible in the visual spectrum.

The activity of the PhD Candidate will start from the study of clinical needs, which will be followed by an accurate technological scouting. Subsequently, after having identified the available technologies in the field of sensors and signal processing, the PhD candidate will be guided towards the development of a functional proof of concept to be tested in a realistic surgical scenario for real clinical validation.

One of the technological difficulties is represented by the extreme miniaturization of the device (about 1 cm), as well as the identification of effective techniques for superimposing the various types of signals and contrast agents: Multispectral, TOF, Fluorescence.

The PhD will work under the guidance of highly experienced surgical team and in close contact with the engineering team of the company. 
The PhD will work in multidisciplinary areas related to ideation, development, product, process and testing.
The skills that will be developed during the PhD are inherent at clinical engineering in surgical robotics application, as well as the physical principles that underlie multispectral vision systems. The project involves the acquisition of skills related to mechatronic systems including firmware and software and the implementation process. In cooperation with Valuebiotech team some AI Model Base algorithms will be developed.

Contact: Vincenzo Maria Sglavo (UniTN), Antonello Forgione (ValueBiotech srl)

DeLonghi Appliances s.r.l- 1 PhD Executive

AE - Circular Procurement and Supply Chain– the role of procurement in circular economy implementation.

This research aims to explore the barriers and enablers of effectively implementing a circular economy model within organizations and how the functions need to interact in a different way to succeed.  A particular focus will be placed on investigating the role of procurement in driving circularity. Furthermore, the study will examine the necessary transformations in the supply chain network, including buyer-supplier relationships and the involvement of other stakeholders, to effectively realize a circular economy 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: marco.formentini [at] (Marco Formentini) (UniTrento), fabrizio.fusco [at] (Fabrizio Fusco) (DeLonghi)

SolydEra S.p.A. - 1 PhD Executive

BE - Ammonia as a fuel and energy carrier. Material characterization for efficient use and clean production

In the framework of an increasingly necessary energy transition and the consequent requirement of new fuels and efficient renewable energy storage typologies, ammonia could be an actual possibility.
For its physical and chemical features, ammonia could be an ideal carbon-free fuel and energy carrier. 
The use of ammonia as a fuel seems to be very interesting in solid oxide cells (SOCs) since NOx and pollutants can be avoided while green ammonia can be synthesized with the solid-state ammonia
synthesis (SSAS) technology. Particular attention has to be focused on the early-stage technology of the proton ceramic cells (PCCs) thanks to its specific advantages if compared to the commercial
oxygen ion one. This PhD aims to study the PCCs considering both ammonia usage as a fuel and ammonia synthesis. The working conditions and atmospheres will be analyzed while the cell
materials will be studied and optimized to obtain suitable features to work with ammonia and allow the synthesis process. New materials will be studied such as ceramic coatings to prevent nitrification
and reduce degradation when ammonia is exploited.

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: vincenzo.sglavo [at] (Vincenzo Maria Sglavo) (UniTrento), dario.montinaro [at] (Dario Montinaro) (SolydEra S.p.A.)

Novurania S.p.A. - 1 PhD Executive

CE - Methods for optimization and industrialization of cross linking process

At present, the standardization approach of the vulcanization process of a coated fabric follows a “trial and error” approach. It is based on the drafting of parameters deriving from other previously standardized parameters for similar materials (compounds of the same family of elastomers), similar construction (number of layers and their thickness) and similar surface area (height and length of the roll).
The first production is completed with these test parameters and the material is sampled and tested in the laboratory (complete mechanical and chemical-physical tests).
The characterization data collected and any feedback given by one or more customers tell whether the properties of the material satisfy the set targets and allow to confirm and standardize all the tested parameters and, if not, to deduce new ones, to be set with a certain confidence interval and which allow for a positive result.
Almost all the manufacturing processes of a rubberized fabric involved can be limited to small test units (in terms of length of materials), but the vulcanization process is the only one that must be tested on units as close as possible to those that are intended to have in the standard process. This to ensure that the characteristics of the tested sampling are then always replicable in the standard process.
Often, it is likely that these standard units extend for hundreds of meters and the convenience of testing of these amounts is not always accessible to all customers. A predictive theoretical setting of this process would allow to Novurania to have an innovative methodology in the development of its products and to have an important flexibility in its standard processes depending on the possibility of varying the standard lengths of its vulcanization units.
The problem that Novurania has is that, at the moment, it does not have the possibility to set up its tests on new products or to limit its standard productions to reduced length units that have a qualitative technical result equal to those of standard length productions. The objective is therefore to develop a theoretical modeling of the vulcanization process, which has as an output a roll length vs pressure curve which would allow to easily predict and calculate changes functional to the purpose. 

The Ph.D. student will have to design a model able to mime the crosslinking process of industrial-scale rubber tissues, he/she will optimize it by conjugating theoretical models with experimental analysis in the company. The Ph.D. student will be involved in the development and optimization of products deriving from new formulations. The company will hire the student for the period of the Ph.D. and will introduce him/her to the field of company research and development.

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: claudio.gioia [at] (Claudio Gioia) (UniTrento), luca.tubiana [at] (Tubiana Luca) (UniTrento), cesare.antolini [at] (Antolini Cesare) (Novurania)

MEMC Electronic Materials SpA - 1 PhD Executive

DE - Wafer shape prediction via correlation with slicing parameters

One of the key operations in producing silicon semiconductor wafers is slicing segments into wafers, via fixed abrasive wire saw, where a diamond coated wire is the slicing vehicle.
Slicing is impacting wafer quality mainly for mechanical / geometrical parameters, like warpage (wafer general deformation), waviness (short range surface modulation) and nanotopology (NT, a kind of local
Any wire saw machine has a set of thermal and mechanical parameters, which are monitored and stored during cutting. The proper monitoring, analyses, and the possibility to link them to the wafer shape could help to improve wafer quality and the possibility to predict and influence the saw performances. The objective of this research is to study the wafer shape in comparison with key saw parameters and
generate a model to be able to predict warp, waviness and NT based on saw behavior, and to change or modify process settings to modulate the wafer shape.
A further refinement of this machine learning approach could come from the study of the main physical laws ruling the cutting process, connect them to the saw parameters and link both to the wafer shape.

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] (Iacca Giovanni) (UniTrento), lmoiraghi [at] (Luca Moiraghi) (MEMC Electronic Materials SpA)

SONY EUROPE BV Spanish Branch - 1 PhD Executive

EE - Integrating large language models and context-specific models for exploiting commonsense and specialized knowledge

Large Language Models (LLMs) -- also called foundation models -- have quickly gained popularity due to their ability to perform well across a variety of language-based tasks.
This project will critically examine their suitability as source of commonsense knowledge for use in knowledge-guided machine learning (ML) tasks.
In order to do so, new neurosymbolic approaches combining LLMs as knowledge bases with current ML techniques (including the extraction of relevant information from an LLM, and the subsequent injection of that knowledge into the ML process) have to be developed, implemented, and evaluated both from a theoretical as well as an applied perspective; relevant dimensions include system performance, but also trustworthiness and resource efficiency of the resulting methods.
Additionally, the integration of knowledge from more classical knowledge sources (such as domain ontologies) besides LLM-based information will also be considered. Several practically relevant use cases from different domains will be addressed in order to highlight the developed techniques' strengths and weaknesses (as well as corresponding mitigation measures for the latter).

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: serafini [at] (Luciano Serafini ) (UniTrento), Tarek.Besold [at] (Tarek R. Besold) (SONY EUROPE BV Spanish Branch)

Danieli & C. Officine Meccaniche S.p.A. - 1 PhD Executive 

FE - Roll cladding processes and materials in slab casting machine 

Danieli Service products have to work in aggressive environments, characterized by high temperature, high wear, cyclic loads and corrosive agents.
This PhD project aims at enhancing their performance by increasing surface durability.
The problem will be investigated from two different points of view:
•    Finding new solutions by new technologies for coating deposition and/or new hard-facing materials.
•    Determining the best testing approach to verify the new solutions´ durability and applicability to industrial production

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: caterina.zanella [at] (Caterina Zanella) (UniTrento),g.marconi [at] ( Marconi Gianfranco) (Danieli & C. Officine Meccaniche S.p.A.)

FBK - 1 PhD Executive 

GE - Generative models for omics data

Research project aimed at developing generative models for omics data with particular attention to single cell RNA seq, focusing on metrics engineering to test data likelihood, enriching the latent space with biological information, exploring transfer learning for model generalization, implementing algorithms for trajectory estimation and bifurcation analysis, enriching differential gene expression analysis with probabilistic models, and developing robust approaches for personalized medicine using optimization and reinforcement learning algorithms. The proposed models will be applied to various datasets, including viral and bacterial infections and cancer, in order to validate their ability to
recapitulate experimentally observed cellular cascades.
Progetto di ricerca finalizzato allo sviluppo di modelli generativi per dati omici con particolare attenzione al single cell RNA seq, lavorando sull’ingegnerizzazione di metriche per validare la verosimiglianza dei dati, sull’arricchimento dello spazio latente con informazioni biologiche, l’esplorazione del transfer learning per la generalizzazione del modello, l’implementazione di algoritmi per l’analisi di traiettorie cellulari, l’arricchimento dell’analisi differenziale di espressione genica con modelli probabilistici e lo sviluppo di approcci robusti per la medicina personalizzata utilizzando algoritmi di ottimizzazione e reinforcement learning applicati ai modelli generativi. I modelli proposti
saranno applicati a vari set di dati, comprese infezioni virali, batteriche e cancro, al fine di validare la loro capacità di ricapitolare le cascate cellulari osservate sperimentalmente.
PS: the research will be co-supervised by Prof. Gabriele Sales (UniPD)

Required/Preferred Candidate Skills and Competencies:
- Advanced knowledge of the different levels of omics data
- Advanced knowledge of the development, optimization and implementation of predictive models in machine and deep learning
- Good knowledge of manifold learning theory
- Good knowledge of scientific programming in Python, including modern libraries for deep learning (e.g., PyTorch and/or Keras)

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: toma.tebaldi [at] (Toma Tebaldi) (UniTrento), giuseppe.jurman [at] (Giuseppe Jurman) (FBK)

HPA s.r.l. - 1 PhD Executive 

HE - Object detection for industrial applications

In recent years, Object Detection has emerged as a critical task in Computer Vision research, with the potential to enable a variety of applications in different domains.
The present research project aims at developing Object Detection models for a variety of industrial applications such as quality control, tracking and monitoring, safety and security.
The proposed techniques will have to address challenges such as occlusion, scale variation, cluttered backgrounds, low-resolution images, illumination variations, small objects, crowded places, data scarcity.
The models will be optimized for low latency and high accuracy.
The project will involve developing and training custom Object Detection models using Deep Learning techniques, and evaluating their performance in real-world industrial scenarios and challenging conditions.

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] (Nicola Conci )(UniTrento), luca.dipersio [at] (Luca di Persio) (HPA s.r.l.)

Arcadia Sistemi Informativi Territoriali S.r.l. - 1 PhD Executive 

IE - Regional multi-hazard risk assessment in Europe

Impacts from multiple natural hazards are increasing due to the combination of several reasons (e.g. climate warning, increase of exposed population and assets, etc.). Understanding the driving factors and quantifying at high spatial resolution the human and economic impacts caused by natural hazards still faces many scientific challenges.
The research activities will focus on quantifying natural hazards (e.g. drought, windstorms), vulnerability and exposure, and the corresponding risks, at high spatial resolution in Europe. The research aims to derive novel relationships that express natural hazard intensity, exposed people and economic activities and consequent impact. The damage functions will be developed through multivariate statistical analysis between reported impacts from natural hazards, their intensity and spatial coverage and a range of socioeconomic variables. The study area will be the European continent, including the Alpine region. 
The research activity will benefit from the collaboration with the Join Research Centre of the European Commission and their expertise in pan-European multi-hazard risk assessment.

The candidate is required to have experience in the assessment of natural hazards (e.g. floods, drought, windstorm, heatwaves/coldwaves) and their impacts. Preferred skills are: i) extreme value analysis; ii) data analysis (vectorial, raster, and time series), iii) advanced knowledge of software such as Python, R-CRAN and MATLAB.

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: giuseppe.formetta [at] (Giuseppe Formetta) (UniTrento), pmaianti [at] (Pieralberto Maianti) (Arcadia Sistemi Informativi Territoriali S.r.l.)