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and subsequently exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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eager to apply and valorize scientific results in this field in high-tech domains such as semiconductor machines and robots, together with highly innovative companies? Would you like to work in a team of
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start no later than March 2026. Your immediate leader will be the Head of Department. About the project Physics-informed machine learning (a branch of Scientific Machine Learning) integrates principles
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protocols, ITC will focus on the monitoring and response parts, building on many earlier projects revolving around the use of UAV/drones, computer vision and machine learning, change and damage detection, and
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is on analysing first-person descriptions of conscious experiences with the help of machine learning and large language models (LLMs) to identify, compare, and systematize different types of states of
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measurement; Measurement of related tracers (e.g., Radon); Programming (e.g., R, Python) for advanced atmospheric time-series analyses, including machine learning; Skills for presenting research at scientific
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at TU Delft. In this project we also work together with experimental groups at TU Delft and beyond. The Delft Bioinformatics Lab has strong algorithmic and machine learning expertise, with a profound
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theory, discrete optimization and machine learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts
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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design