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testing. A campaign layer, driven by Bayesian optimization, decides which experiment to run next. The postdoc will own the system architecture below that layer: the PLC and instrument control, the software
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. Responsibilities: Conduct research on automated reasoning and proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and optimization. Participate in collaborative research projects
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with national quantum strategies, such as Canada's National Quantum Strategy. The recruited candidate will participate in a Franco-Canadian collaboration focused on the study and optimization
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for developing and optimizing plasma-based functionalization processes for ferrimagnetic nanoparticles. The successful candidate will be responsible for investigating the relationships between plasma parameters
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highly desirable. Experience with computational methods such as density functional theory calculations, geometry optimization, and prediction of NMR parameters will be considered an advantage
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Max Planck Institute for Nuclear Physics, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 2 days ago
the field of theoretical high-energy particle/astroparticle physics Use of analytic and numerical methods to explore opportunities for next generation gamma-ray instruments with a focus on constraining
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physiology, animal models of cancer and cachexia, imaging, histological analyses, and other mechanistic or translational approaches. · Contribute to the development, optimization, and validation
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improve high-throughput experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and
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, support for the “Klimaticket” (climate ticket), discounts at the USI (University Sports Institute) and the Language Center, numerous corporate cooperations Diverse Continuing Education Offerings: i.a
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oceanography, numerical modeling, acoustic propagation, and scientific computing. The postdoctoral researcher will develop, apply, evaluate, and integrate uncertainty modelling into the predictions