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, use imitation learning algorithms to learn pick-and-place actions, design HRI experiments with users, evaluate data, and share the code and benchmarks in open repositories. This postdoctoral position is
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to develop novel AI and machine-learning methods for accelerated materials discovery. The focus is on combining generative AI, active learning, first-principles simulations, and machine-learning potentials
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. The work involves the development of electromagnetic models, numerical simulations, prototype realization, and measurements, as well as dissemination of research results through leading international
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letter summarizing your qualifications, scientific interest, and why you are applying for the position (max 1 page), Curriculum vitae (CV) with publication list, Copy of doctoral degree certificate
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in day-to-day work. Strong computational and numerical modelling skills, including finite element or multiphysics simulation and scientific programming. Awareness of diversity and equal treatment
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product kinetics by ion implantation of advanced nuclear fuel (Uranium Nitride). The project is supported by the Swedish Radiation Safety Authority (https://www.stralsakerhetsmyndigheten.se/ ) and in
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to the instructions in the ad. The application must include: CV including relevant professional experience and knowledge. Copy of diplomas and grades from your university studies. Translations into English or Swedish
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professional experience and knowledge. Copy of diplomas and grades from your university studies. Translations into English or Swedish if the original documents have not been issued in any West-European languages
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modelling, geospatial data science, generative modelling, mobility data analysis, or transport simulation, especially when combined with knowledge of complex systems, network science, resilience theory, urban
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at large scale research infrastructures Experience in in vitro experiments to characterise nanoparticle therapeutic efficacy Experience in in molecular dynamics simulations (coarse grain or all atom) and