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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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data. Develop and apply machine learning models to estimate uncertainty in climate impact statements. Analyse spatial and temporal patterns and trends in climate-extreme impacts. Cross-validate
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yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in
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programming task within the thematic context of the advertised position: https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bi-molecular-machine-learning/ Employment conditions This is a
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check our website for more information: https://www.uni-goettingen.de/de/635183.html PhD students with their own funding (e.g. DAAD) can join at any time. Tuition fees per semester in EUR None Combined
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applicant has a strong background in bioinformatics and/or probabilistic machine learning, as well as experience in omics data analysis, and possesses solid English-language skills. Experience with
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for improved understanding of structural and kinetic processes in electrolytes; and machine learning concepts for improved analysis of experimental and simulated data. Material Synthesis Within this research
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 23 days ago
new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in
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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Develop new machine learning
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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