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models. The scientist will conduct research using machine learning and classical parameterization methods on data from ocean gliders equipped with microstructure turbulence sensors, turbulence resolving
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community around machine learning of the SCADS.AI center (https://scads.ai ) and the recently granted Excellence Cluster REC² – Responsible Electronics in the Climate Change Era. We aim to attract the best
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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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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multi-omics data integration and the project will provide opportunities to learn, develop, and apply machine learning and deep learning methods on genomics data. Requirements: excellent university and PhD
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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or infrastructure. This is what makes our daily work so meaningful and exciting. The Division of Computational Genomics and Systems Genetics is seeking from October 2025 a PhD Student in Deep Learning for Rare
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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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reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl