20 computational-physics-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" Postdoctoral positions at ETH Zurich in Portugal
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systematically. In the second thrust, the student will develop theoretical and computational frameworks, in close collaboration with Prof. Vlasios Mavrantzas (a collaborator in the lab), to capture entropy
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. Publish your findings in high-quality scientific journals and present them at international conferences. Profile You bring a strong background in quantum physics, computational science and a genuine
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theoretical and computational methods to fundamental problems in materials growth. A strong background in thermodynamics, kinetics, statistical mechanics, solid-state physics, physical chemistry, or
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www.inspire.ch and the Automatic Control Lab https://control.ee.ethz.ch . Curious? So are we. Please send your full application to our HR department [email protected] with the reference “ILC Postdoc
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), whether through remote sensing or direct onsite measurements, requires significant innovation. These challenges demand close collaboration across physics, biology, chemistry and the earth sciences. NCCR
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Workplace We offer The position is hosted in the Department of Mechanical and Process Engineering (D-MAVT) of the ETH Zurich, in the Zentrum Campus in Zurich, Switzerland. D-MAVT (https://mavt.ethz.ch/) is
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. Do you have a broad interest in physics and technology? Do you like the prospect of working in an experimental project in close collaboration with theory? Are you creative, persevering, independent
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the emergence of life in the Universe which gives access to a Switzerland-wide, deliberately interdisciplinary network spanning planetary physics, geochemistry, chemistry and biology. The position
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unexplored, providing exceptional opportunities to discover new physical phenomena and establish new concepts in condensed-matter physics. Job description You will design, build and further develop advanced
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problems. Project background The current era of artificial intelligence is predominantly driven by advances in computational power and infrastructure. As models scale to unprecedented sizes, their