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... Your PhD research focuses on several topics like the development of a novel prognostics model based on the evaluation of sensor data and for this specialized methods. Purpose is the updating of digital
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through simulation Utilize advanced CMOS technology nodes (28nm, 22nm, and below) Automate the design and layouts using Skill programming Develop behavioural models for circuit verification Contribute
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, many material properties are still only poorly known. We offer to contribute to the ongoing research in this fascinating region. Especially, we are looking for somebody to simulate seismic wave
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science, physics, computational science or similar, with experience in modeling and simulation Strong understanding of the (computational) mechanics of solids and the finite element method and/or spectral
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, you will benefit from the combined expertise in quantum algorithms and materials simulations at the institutes PGI-12 and IET-3. Thereby, Forschungszentrum Jülich will provide you with an ideal
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on mechanistic study of high cor
to do a PhD and why you are suitable for this position. CV (max 2 pages) A certified copy of your master's degree and your course grades. Copies (or drafts) of your master's thesis and other documents
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gain a unique skill-set at the interface between: EPR spectroscopy and redox flow battery electrode science, Modelling and hardware prototyping, Multiphysics simulations and experimental validation
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to do a PhD and why you are suitable for this position. CV (max 2 pages) A certified copy of your master's degree and your course grades. Copies (or drafts) of your master's thesis and other documents
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on mechanistic study of biodegra
. Your application must include: A short cover letter (up to 1 page) in which you describe yourself, why you want to do a PhD and why you are suitable for this position. CV (max 2 pages) A certified copy
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link structure-property relationships from DFT, MD, phase-field, TEM/SEM, and other multimodal datasets from simulation and experiment Develop benchmarking protocols and toolkits to evaluate AI models