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properties changes. - The demonstration of the tear detection with machine learning classification applied directly on S-parameters of the MWI system without solving the inverse problem. The objective
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of the art data science approaches (text mining, machine learning, AI) to comprehensively highlight yet undiscovered virus/host/environment relationships and annotate potentially putative new spillover
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. The objective of this thesis project is to develop hybrid models that integrate electrochemical principles with machine learning techniques to analyze data from electrolyzers, predict performance, assess lifespan
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parameter estimation using Bayesian inference, and/or the exploitation of Machine Learning (ML) based algorithms to reduce false positives caused by human generated interference signals in the observational
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technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
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by exploiting foundational machine-learning potentials such as MACE, SevenNet, or Orb-V3. The predictions will then be progressively refined and verified by DFT and, ultimately, tested experimentally
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-flexible technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their
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machine learning approaches to integrate single cell and spatial analysis in order to identify molecular signatures and pathways underlying radiation-induced effects. Collaboration: Work in close
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(formulation, algorithms, applications in structural mechanics), HPC computing, reduced-order modelling, machine learning, Vibrations and structural dynamics, architected materials, Additive manufacturing