365 computational-physics "https:" "https:" "https:" "https:" "University of Cambridge" positions in Switzerland
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where the decision process is clear; (iii) fairness and non-discrimination: designing algorithms to make unbiased decisions by mathematically defining fairness (e.g., demographic parity, equality
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by beam (e.g. laser light, electrons) and plasma induced processing. LAMP provides full cycle technology development, starting from novel materials, deep understanding of the background physics of beam
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motivated team player who thrives in a collaborative and interdisciplinary research environment. The ideal candidate possesses a PhD degree in chemistry or chemical engineering, materials science, physics
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will require a high degree of innovativeness in binder research, including mastery of asphalt binder chemical and mechanical characterization, production process optimization while consid-ering
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23 Mar 2026 Job Information Organisation/Company Paul Scherrer Institut Villigen Research Field Physics Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline
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job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD position – Source
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physics Physics » Biophysics Physics » Chemical physics Physics » Computational physics Physics » Condensed matter properties Physics » Electromagnetism Physics » Mathematical physics Physics » Optics
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Doctoral position: Innovation economics and policy for climate
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of emerging technologies balanced against their dark sides. Our research at the intersection of data, behavioural, and computer science draws on data from real-world and experimental settings The Centre for AI
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models Lead the design and implementation of innovative methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs