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data. Manage preferences for further information and to change your choices. Accept all cookies Reject optional cookies Skip to main content Faculty Position in Experimental Particle Physics Employer
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environments. The project will explore how learning-based and agentic AI systems (e.g., agents built on large language or foundation models) can be combined with physics-informed process models, digital
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background, and an outstanding MSc degree in Engineering, Computer Science, Physics, Applied Mathematics, or a related field. You should be proficient in or willing to learn generative deep learning – in
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detect reliable traces of life (biosignatures) – whether through remote sensing or direct onsite measurements – requires significant innovation. These challenges demand close collaboration across physics
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learned control policies and neural representations of movement in collaboration with experimental labs Profile Common requirements: PhD (completed or close to completion) in computer science, applied
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technology transfer activities Profile PhD in microengineering, electrical engineering, mechanical engineering, applied physics, materials science, or a closely related field Strong hands-on experience
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use of solar energy. https://www.epfl.ch/labs/sci-enac-as/ Main duties and responsibilities As a PhD Student, you will be expected to: Assume full responsibility for your PhD dissertation; Perform
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are most important for the use of solar energy. https://www.epfl.ch/labs/sci-enac-as/ Main duties and responsibilities As a PhD Student, you will be expected to: Assume full responsibility for your
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, including physics-based modeling across scales (discrete/mesoscale/continuum), thermodynamics-informed machine learning and neurosymbolic AI. They will also have the opportunity to lead international
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Quantitative Finance at EPFL seeks postdocs to reinforce our research in quantitative finance with a focus on applied machine learning. Main duties and responsibilities Working and collaborating on research