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Your Job: We are looking for a researcher to develop and apply machine learning models for genomic data in our lab. We focus on sequence analysis, genomics, semantics, and cross-domain data
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
Area of research: Scientific / postdoctoral posts Starting date: 01.07.2025 Job description: Postdoc (f/m/d): Machine Learning for Materials Modeling With cutting-edge research in the fields
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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medical machine learning for a talented postdoctoral researcher (f/m/d) to deepen their expertise and interest in machine learning for medical image analysis and build their early scientific career. About
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The Network Analysis and Modelling group investigates how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. Using machine learning and network
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. The current project deals with the use of machine learning / artificial intelligence and big data science in the field of synchrotron research. Development and application of machine learning / artificial
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Management Technologies at TUM’s School of Engineering and Design is looking for a doctoral researcher (f/m/d) in the area of Collaborative Machine Learning for the Energy Transition. You are passionate about
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Technology (CIT) and TUM School of Medicine and Health is offering a 2y-4y postdoctoral full-time position in medical machine learning. The Computational Pathology Lab (https://schuefflerlab.org
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19.07.2022, Wissenschaftliches Personal The Machine Learning and Information Processing group at TUM works in the intersection of machine learning and signal/information processing with a current
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this interdisciplinary project, we are looking for a strong candidate to contribute to the development of quantum algorithms and applications, focusing on quantum walks and quantum machine learning on graph structures