36 parallel-processing-bioinformatics-"Multiple" PhD positions at Forschungszentrum Jülich
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: Experience in organic semiconductor processing and characterization High degree of independence, motivation and reliability Excellent ability to cooperate and work in a team Curiosity and fast learning are a
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Your Job: The conventional, manual co-design of algorithms and hardware is slow and inefficient. Our group develops methods and tools to automate the co-design process. The core of this project is
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secondments) and attend conferences and network activities If applicable, further desirable qualifications: Experience in organic semiconductor processing and characterization High degree of independence
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: A completed university degree (Master or equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep
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slow and inefficient. Our group develops methods and tools to automate the co-design process. The core of this project is the development of meta-optimization techniques that can automatically search
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equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep learning, or signal processing; familiarity with
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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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investigating the physical properties of the molecules in the laboratories on the campus of Forschungszentrum Jülich. The investigation of how these characteristics are influenced by processes in the value chain
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Your Job: At the Electrocatalysis department of Prof. Karl Mayrhofer, we offer a PhD position within the team Nanoanalysis of Electrochemical Processes. Lead by Dr. Andreas Hutzler, the team is
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-based processing. This project will investigate event-driven learning approaches in the context of RL in an event-triggered fashion. Data efficiency will be improved by using meta-learning and pre