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technologies. At HPI, you benefit from close supervision by experienced professors and postdocs, cutting-edge research facilities, and a collaborative academic network, e.g. with the University of Cape Town (UCT
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digital technologies. At HPI, you benefit from close supervision by experienced professors and postdocs, cutting-edge research facilities, and a collaborative academic network, e.g. with the University
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, physics, or similar, with a strong Machine Learning or simulation background In depth practical experience in at least one programming language (preferably Python) Ideally, some practical experience in
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. He/she/they will learn and apply state-of-the-art molecular and cell biology technologies established in our team, ranging from in vivo disease models to multi-omics and single cell analysis
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). The institute also maintains locations in Dedelow and Paulinenaue. The research group “Farm Economics and Ecosystem Services” is a team of dedicated postdocs and PhD students who want to make a difference through
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dynamics, data science, and machine learning are beneficial. Please submit your detailed application with the usual documents by August 15, 2025 (stamped arrival date of the university central mail
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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Max Planck Institute for the Study of Societies • | Koln, Nordrhein Westfalen | Germany | about 17 hours ago
you will learn the specific methods you need for your project Feedback from experienced research advisers Excellent research facilities Instruction in English Thesis may be written in English or German
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
students with strong theoretical foundations and a desire to contribute to fundamental algorithmic research. Our group works at the intersection of algorithms, machine learning, and interactive visual
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the application of machine learning (ML) methods or large language models (LLMs) Proficiency in Python programming and confident use of Unix/Linux environments; ideally experience with version control systems (e.g