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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 code: 980 - 2026/WD 1
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experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image data analysis A willingness to engage in interdisciplinary scientific work
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Science, Machine Learning, Computational Linguistics or a related field, if applicable with PhD previous experience in Natural Language Processing, knowledge Graphs, Machine Learning or Recommender Systems
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, a novel spatial discovery proteomics concept that integrates microscopic cell phenotyping with deep-learning based image analysis and global MS-based proteomics. This unique method was recently
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at international conferences. Your profile PhD (degree or almost completed) in Physics, Applied Mathematics, Complex System or a related field. Strong background in mathematical modeling, machine learning
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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 Developing new machine learning
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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 the opportunity to work with
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of artificial intelligence, machine learning and/or deep learning experience in scientific publishing and presenting research results knowledge or experience in public health research Personal skills Independence
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recognition, and enable seamless collaboration between humans and machines. Long-Term Human-Technology Evolution: investigate the longitudinal impact of human-technology interaction on learning, behavior, and
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research environment for biophysics. Our group combines molecular dynamics simulations with machine learning techniques to understand how proteins, biomembranes, and small drug-like molecules interact