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of beyond Cold and Collisionless Dark Matter simulations. The position will be to leverage physics-informed neural networks to accelerate simulations of Fuzzy/Axion Dark Matter. However there will be
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engineering or physics and have experience in experimental fluid dynamics and wind energy. It is highly desirable that the candidates have experience in the use and analysis of data from scanning Doppler wind
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-level academic education and a proven track record in conducting and publishing outstanding research in the field of physical metallurgy. Skills of direct interest include a strong command of the
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’ developments and key results Your profile : PhD in photonics, semiconductor physics, electrical engineering or closely related field with high academic record indicated by publications in peer-reviewed
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(or Physics); furthermore, very good computational skills and excellent knowledge of English are desirable. Applications should be submitted through the EPFL application portal and include motivation letter (1