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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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through soft, disordered materials, including auto-regulated networks, composite soft solids, and exotic photonic biomaterials. The lab has two fully funded PhD and/or postdoctoral positions available
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, particularly in C++ and Python Good communication skills in spoken and written EnglishInterest or prior experience in machine learning techniques is considered an asset. You may expect a multifaceted and
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is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and the AI Health Innovation Cluster ). Your
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cutting-edge big/deep data analysis methods, including machine learning and artificial intelligence. The ideal candidate will therefore have a strong background in data science and in the application and
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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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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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
apply machine learning/AI methods for ecological analyses Expedition experience Further Information The AWI is characterized by The AWI is characterized by our scientific success - excellent research
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of machine learning and health sciences, with unique access to experimental and clinical data. Embedded in Munich’s thriving AI landscape, fellows benefit from world-class facilities, interdisciplinary
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, you must have a PhD in a relevant field. As a suitable candidate, you have expertise in deep convective cloud processes and experience with scientific data analysis. Prior experience in applying machine