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surfaces machine learning methods, and/or the development of novel solutions to the many-body Schrödinger equation. Applicants with proven experience in the development of new computational methods and their
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Application Deadline 12 Sep 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Hours Per Week 30 Offer Starting Date 15 Oct 2025 Is the job funded through the EU Research Framework Programme
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Hours Per Week 40 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 39000-2025-001541 Is the Job related to staff position within a Research
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:00 (UTC) Type of Contract Temporary Job Status Part-time Hours Per Week 30 Offer Starting Date 15 Sep 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is
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interest in using computational methods such as cognitive and psychophysiological modeling, (Bayesian) statistics and optimal experimental design, and agent-based modeling to address problems in
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for the philological-cultural study of South Asia and Tibet in the German-speaking world and is home of several third-party funded research projects. Through cooperation with numerous academic institutions worldwide
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-based methods and big data applications and machine learning methods. Research focus will be on feedback processes between erosion, sedimentation, tectonics and climate, and topics could include interplay
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better understand our world. Does this sound like you? Then join our accomplished team! Your personal sphere of influence: The Computational and Soft Matter Physics Group at the Faculty of Physics invites
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Programme? Not funded by a EU programme Reference Number 151080-2025-001547 Is the Job related to staff position within a Research Infrastructure? No Offer Description The world is changing at an ever-faster
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, physiological, and neurobiological bases, studying how the brain maintains or changes functionality over time, and probing its evolutionary origins and development. Our PhD program is tailored to individual