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scientific curiosity. You thrive at the boundary of robot learning, computer vision, deep learning, and simulation, and you are excited to see your research running on real robots. You communicate clearly
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. You have: A PhD in Computer Science, Machine Learning, Applied Mathematics, Scientific Computing, Data Engineering, or a closely related field. Demonstrated ability to conduct high-quality academic
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feed into this vision. The intended start date is July–August 2026. Job requirements PhD in machine learning, artificial intelligence, computational chemistry, computational materials science, or a
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of receiving their PhD. In particular for this position, the following is required: PhD in data science, AI, computer science, machine learning, Earth system science, climate etc., with a thesis subject relevant
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from the areas of few-shot learning, continual learning and modular deep learning, as well as different LLM alignment frameworks, based on reinforcement learning and direct preference optimisation
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perception systems, using deep learning and simulation-to-real domain adaptation techniques. You will work with a multidisciplinary team, contributing to fundamental and applied research. Your role will
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the dispersion of these macroplastic items in these flow fields; comparing the results of the simulations to results from an experimental campaign of floating trackers; collaborating with a postdoc and two PhD
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provides opportunities to strengthen your academic profile through (co-) supervision of PhD, MSc, and BSc students, as well as teaching (if desired). Especially attractive is the opportunity to collaborate
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skills: Good knowledge of ML/AI based techniques to develop fast surrogates (deep neural networks) and capability to develop own efficient model learning schemes (deep learning techniques, representation
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Employment 0.8 FTE Gross monthly salary € 4,728 - € 6,433 Required background PhD Organizational unit Faculty of Social Sciences Application deadline 24 March 2026 Apply now A driven postdoctoral