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Field
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remains challenging, limiting multiscale imaging approaches in near-field holotomography. To address this, the PhD project combines machine learning, high-performance computing, and synchrotron-radiation
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samples, phase retrieval in this regime remains challenging, limiting multiscale imaging approaches in near-field holotomography. To address this, the PhD project combines machine learning, high-performance
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. Disseminate research through high-impact publications and conference presentations. Requirements: PhD (or near completion) in Imaging Science, Computational Biology, Bioinformatics, Machine Learning, Data
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06.12.2021, Academic staff The professorship of Data Science in Earth Observation is seeking six new PhD candidates/PostDocs for its new center for Machine Learning in Earth Observation (ML4Earth
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applications, although this is not the primary goal of the position. What you will bring (position requirements): A PhD in machine learning or data science and a background in computer science, physics
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: · A completed M.Sc. degree in computer science, machine learning, and related fields. · Strong proficiency in English (the working language of the institute). · Capability and willingness
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fully funded PhD position in the area of safe data-driven system identification for cyber-physical systems, offered by our research group at the intersection of control theory, machine learning, and
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thesis is not pre-defined and should be defined by the candidate over the course of the first year. The domain of the PhD thesis must be machine learning and either control theory, path planning, or multi
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- When and where do we reach the limits of adaptation to riverine flood risk?”. You have experience in machine learning, programming and flood risk research. If so, we encourage you to apply! You will
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD