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& Analysis Perform quality control, alignment, and quantification of bulk and single-nucleus RNA-seq datasets. Conduct differential expression, clustering, trajectory inference, cell type annotation, and
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Bayesian inference framework for identifying complex aerospace systems combining with limited experimental data. It can be also used to quantify uncertainties from experimental testing, significantly
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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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methods for causal inference in observational data, is strongly preferred. Using various existing large datasets with rich information for knowledge synthetisation and triangulation over the course of the
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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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Bayesian methods, deep learning, deep generative models, reinforcement learning, graph neural networks. Interviews are expected to happen in July 2025. Applicants are encouraged to guarantee that referees
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to the requirements for learning or inference by various AI modules deployed in the devices and the network. This line of work will investigate the fundamental tradeoffs among latency, accuracy, and energy efficiency