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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework
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15 Jan 2026 Job Information Organisation/Company Paul Scherrer Institut Villigen Research Field Physics » Condensed matter properties Researcher Profile First Stage Researcher (R1) Positions PhD
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solutions for the global challenges of today and tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2026/phd-position-in-physics-inform… Requirements Research FieldComputer
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of electron spectroscopy experiments Modelling of experimental data Your profile The position is immediately available for a candidate with a master's degree (or equivalent) preferably in physics, physical
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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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system models to analyse whether spatially coherent urban and energy configurations can be operated efficiently under realistic physical, spatial, and infrastructural constraints. The work aims to identify
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through the electronic application process. Each recommendation consists of two elements: A form to be filled out by the referee An additional letter with the referee’s comments Your referees
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that will shape your research career. Your profile We are looking for 2 highly motivated PhD students with a strong analytical background and an MSc degree in Physics, Computational Chemistry, Materials
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with project partners and disseminate results through reports, visualisations, and scientific publications Your profile You hold an M.Sc. in Data Science, Computer Science, Engineering, Physics
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of students Your profile University Master's degree in process engineering, chemical engineering, mechanical engineering or materials science with some (CFD) experience. Good technical knowledge and/or high