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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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, Computer Science or related fields (for PhD); Doctorate in Physics, Computer Science or related fields (for Post-Docs). The positions are funded via the Cluster of Excellence (Machine Learning for Science), the ERC
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 19 days ago
holography. We are seeking a highly motivated postdoctor-al researcher to join our multidisciplinary team at the intersection of optics, electronics, machine learning, and atmospheric science. The successful
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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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student assistants and contribute to shaping the CRC’s research direction Your Profile PhD in computer science, neuroscience, machine learning, or related field Strong programming skills in Python 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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following position Postdoctoral researcher (m/f/d) in Environmental Data Science and Machine Learning for the project BoTiKI Location: Görlitz Employment scope: full-time (40 weekly working hours) / part
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in an area of safe machine learning and/or applications in healthcare Management of a team of PhD students, postdocs, and software developers Coordination of the implementation of research prototypes
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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