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Field
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. This will involve investigating techniques for model compression and efficient inference to enable on-board condition monitoring directly at the wind turbine, reducing data transmission requirements, central
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recent large-scale capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning. Explainability, leveraging identifiability and unique recovery
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development for scalable and green AI. Use cases in
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-mechanical phase-field model incorporating hydrogen diffusion, mechanical degradation, and fracture evolution. - Employ physics-informed neural networks (PINNs) to infer hidden fields and accelerate
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. In particular, the research project will focus on inferring trajectories from spatial transcriptomics data modelling at the same time the cells evolution in gene expression and in space. Required
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modelling (inc. mixed effects regression models and/or Bayesian statistics). You have experience in conducting empirical research (e.g., experimental design, stimuli selection, recruitment, participant
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Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with
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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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sequences, analyse those data using Bayesian, Maximum Likelihood and coalescence approaches, and build matrices of geolocation and morphological data. The work will be alongside others working on related