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Field
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Artificial Intelligence (AI) and Machine Learning (ML). In this position students will contribute to research projects in CKL and as part of their education, will also engage in a dedicated 6-months internship
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, machine learning, energy technology or related subjects Prior experience in building predictive models using regression techniques, neural networks (CNN, GNN) or symbolic regression Experience in
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for part-time employment. Starting date: 14.01.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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Job related to staff position within a Research Infrastructure? No Offer Description PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1
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. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning. Your tasks: Development and comparison of data driven models for the prediction of stresses in
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with, enhance or replace established methods
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– from the modeling of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference
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11.12.2025 Application deadline: 15.02.2026 The Faculty of Science at Tübingen University invites applications for a W3-Professorship in Machine Learning in Physics at the Department of Physics (m/f
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Your Job: Modeling and characterization at molecular level of selected biological processes by performing classical molecular dynamics, and employing enhanced sampling methods and machine learning