54 high-performance-computing-postdoc PhD scholarships at Forschungszentrum Jülich in Germany
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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on materials science tasks as well as integrate your semantic-AI services into high-throughput GPU/HPC workflows, contributing to data management, metadata structuring, and semantic annotation Collaborate with
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of software tools and concepts. Supervise student projects and BSc/MSc theses. Your Profile: Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field
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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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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
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of structure-performance relationships of different catalyst and membrane surfaces Development and validation of various test stand modifications Coordination with internal and external project partners from
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for an ideal balance between stability, performance and price Building a test station for evaluation of electrochemical performance in short stack Physical, spectroscopic, and electrochemical characterization
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operation Participation in project meetings. Coordination with internal and external partners Publication and presentation of research results in relevant journals and at international conferences Your
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outstanding key performance indicators. Your tasks in detail: Contribute to develop an innovative process for the autothermal dehydrogenation of LOHC using fundamental knowledge of chemical engineering
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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