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computational physics and probability theory and great interest in interdisciplinary research and collaboration with experimental groups. You hold (or expect to complete soon) a Masters or equivalent degree in
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degree / double degree programme Yes Description/content The statistical physics of complex systems is a very broad field ranging from the study of quantum phenomena to the conformational behaviour
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PhD programme with a focus on data science and research in the structure of matter. The graduate school covers data challenges from application fields such as structural biology, particle physics
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papers Contributing to educational events, such as courses and hackathons Your Profile: Excellent Master or preferable subsequent PhD degree in Computer Science, Mathematics, Physics, or similar fields
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their system-level integration Develop design architecture and break down requirements into functional blocks Create and execute test benches for RTL and timing simulations Perform formal verification
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in geophysics, physics, geoscience, computational geoscience, or related natural sciences with an overall grade of at least good Experience in programming (e.g., matlab, phyton, C/C++) and parallel
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schematics, layout, test benches and simulation Design blocks as Op-amp, LDO, VCO, PLL, voltage or current reference circuits Develop innovative analog design solutions to enhance signal integrity, noise
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Max Planck Institute for Solid State Research • | Stuttgart, Baden W rttemberg | Germany | 3 days ago
Institute for Chemical Physics of Solids and Max Planck Institute for the Physics of Complex Systems in Dresden, Max Planck Institute of Microstructure Physics in Halle, Max Planck Institute for the Structure
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-driven simulation with physics-inspired data and image analysis, often in close collaboration with experimental partners, to identify physical principles behind biological dynamics and self-organization
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, sense amplifiers or memories Implement and verify circuit layouts through simulation Utilize advanced CMOS technology nodes (28nm, 22nm, and below) Develop behavioural models for circuit verification