50 high-performance-computing-postdoc PhD positions at Forschungszentrum Jülich in Germany
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design are often too slow, costly, and inefficient to cope with the increasing complexity of performance and resource-efficiency requirements. This collaborative doctoral project brings together
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practices. Within this framework you will: extend and use a process-based modeling approach which explicitly represents microorganisms and biomolecule functioning in soil systems. use process-based modeling
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Devices - Photovoltaics) and high-performance computation (IET-3: Institute of Energy Technologies - Theory and Computation) towards the overarching aim of implementing an inverse design approach for novel
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simulation studies (LTspice and Cadence Spectre) perform algorithm-circuit co-design, quantifying performance and benchmarking with competing approaches support printed circuit board design and tape-out
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very latest issues that impact our society
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PhD Position - Organic Electrosynthesis: monitoring of reaction transients with real-time techniques
for cooperation with excellent partners at the FAU Erlangen-Nürnberg, the FZ Jülich, RWTH Aachen, and numerous partners in Germany and abroad An excellent international environment to perform sound, high-quality
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morphology with its performance (reactivity, selectivity, efficiency) and degradation under realistic long term deployment conditions Coordination and execution of in-house beam times Collaboration with other
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collaboration with a team of experts at FZJ (INM-9: Institute of Neuroscience and Medicine - Computational Biomedicine, IBI-1: Institute of Biological Information Processing - Molecular and Cellular Physiology
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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
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using MPI and high-performance computing resources is advantageous, but not necessary Your application should include a CV, motivation letter, copies of university degrees and grades, and contact