PhD POSITIONS

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MASTER THESES

Theses with international collaborations:

Theses on risk and resilience assessment of complex systems and critical infrastructures:

Guided probabilistic risk assessment of complex systems using reinforcement learning optimization (with UCLA, USA)

Resilience assessment and management of power grids in response to extreme weather conditions (heat wave, storm, flood, wildfire, etc.) (with UCLA, USA)

Optimal sensor placement for efficient state and performance monitoring of interdependent complex systems, Case study: power grids (with UCLA, USA)

Exploration of optimization methods for guided probabilistic risk assessment of complex systems (with UCLA, USA)

Development of a neural network for Critical Heat Flux predictions (with CEA, France)

Physics Informed Deep Learning to Improve Predictive Capabilities, Interpretability and Reduce Uncertainty of Modeling and Simulation of Nuclear Microreactors (with MIT, USA)

Real-Time Condition-Informed Safety Assessment of a Microreactor (with MIT, USA)

Inverse Uncertainty Quantification with Deep Learning Surrogate Models of
Accident Scenarios in Nuclear Microreactors (with MIT, USA)

Development of a Digital Twin of a Nuclear Microreactor: on the Multi-fidelity
Uncertainty Quantification (with MIT, USA)

Modeling the Competing Diffusion of Information vs Disinformation and Resulting Infrastructure Impacts (with University of Oklahoma, USA)

Identifying Relationships Between Information and Physical Layers with Signal Analysis (with University of Oklahoma, USA) 

Advanced methods of dynamic risk assessment for energy systems (with Leibniz University of Hannover, Germany)

Extended Survival Signature Approach for Multi-state Systems (with Leibniz University of Hannover, Germany)

Semi-Supervised Class-Imbalance Learning for Identifying Rare Events (with University of Windsor, Canada)

Safety analysis of nuclear power plants: quantification of the uncertainty in nuclear thermal-hydraulic codes based on heterogeneous experimental data

Data adequacy assessment in thermal-hydraulic experimental tests of Intermediate Break Loss of Coolant Accident (IBLOCA)

Theses on reliability assessment:

Development and implementation of uncertainty propagation methods for the estimation of early-life failure probability of semiconductor devices (with INFINEON)

Advanced Monte Carlo simulation for efficient dynamic reliability assessment of polymer electrolyte membrane fuel cell systems (with Leibniz University of Hannover, Germany)

Development and implementation of Tools for the Availability and Reliability Assessment of the CERN Complex Technical Infrastructure Systems (with CERN, Switzerland)

Theses on Prognostics and Health Management for Predictive Maintenance:

Artificial Intelligence-Based Prescriptive Maintenance by Federated Learning (with CNR and CERN)

Development and implementation of prognostics and health management methods based on image processing (with INFINEON)

Uncertainty-driven AI methods for Prognostics and Health Management (with ETH Zurich, Switzerland)

Development of State-of-Health Indicators for Researchable Batteries Using Deep-Transfer Learning Methods (with Ecole Polytechnique, France)

Deep Transfer Learning Methods for Prognostics and Health Management (PHM) of Batteries (with Tsinghua University, China)

Theses with industrial partners:

Theses on risk and resilience assessment of complex systems and critical infrastructures:

Electrical network resilience (with ARAMIS Srl, Italy)

Offre de stage au sujet de la diversification des systèmes électriques du nouveau nucléaire (with EDF, France)

Benchmark of dynamic methods for Probabilistic Safety Assessment of nuclear power plants

Theses on reliability assessment:

Application du « model-based system engineering » (MBSE) aux études de fiabilité-disponibilitémaintenabilité (with ALSTOM, France)

Theses on Prognostics and Health Management for Predictive Maintenance:

Analisi multi-fonte dei dati di risorsa vento per un progetto di impianto eolico (with EDISON, Italy)

Intelligenza artificiale, tecnologie robotiche e big-data per la manutenzione predittiva degli impianti eolici (with EDISON, Italy)

Large-scale multi-modal online health monitoring and fault-tolerant control technology for sensors of the Distribution Control System (DCS) of Nuclear Power Plants (NPPs) (with China Nuclear Power Engineering Co., Ltd)

Train Diagramming (with ARAMIS Srl, Italy)

PHM impact on Life Cycle Cost Analysis (with ARAMIS Srl, Italy)

Other:

Development of an Artificial Intelligence-based Virtual Meter Tool for Carbon Capture and Storage Applications (with ENI, Italy)

Development of machine learning techniques for data-driven DWIM systems (with ARAMIS Srl, Italy)

Physics-informed neural network for modeling high burnup structure formation in nuclear fuel (with NRG, Italy).

Theses internal at LASAR:

Theses on risk and resilience assessment of complex systems and critical infrastructures:

Large Language Models for Risk Assessment of Industrial Systems

Ecology network analysis methods for balancing efficiency and resilience of critical systems and infrastructures

Resilience of energy production plants exposed to Natural-Technological (Natech) scenarios of increasing frequency and severity in the climate change context

Uncertainty quantification methods in the risk analysis of energy systems exposed to NaTech hazards and accident scenarios

Uncertainty Quantification in Computational Fluid Dynamics (CFD) for Risk Assessment

Methods for the evaluation and optimization of the resilience systems, plants and infrastructures

Advanced methods for the risk assessment of industrial facilities and plants in the oil&gas and energy sectors

Climate change impact on the risk assessment of energy production plants

Theses on Prognostics and Health Management for Predictive Maintenance:

Deep Learning Methods for Extracting Information from Text Documents in Prognostics and Health Management Applications

Development and implementation of diagnostic techniques for the classification of abnormal conditions in industrial plants using transparent rules