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. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image
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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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: · 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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modelling, analysis of complex dynamical systems, simulation, analysis of large-scale datasets with machine learning methods, and software development are beneficial Good organisational skills and ability
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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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Systems, Tübingen/Stuttgart in Germany. Together we want to build a lighthouse for machine learning and modern artificial intelligence in Europe. The cooperation spans all levels, from leading experts
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in close collaboration with domain and machine learning experts. The position is expected to start in June 2026 and will be embedded in collaborations across the ELLIS Unit Jena. Part-time work is
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processing parameters. You will develop machine learning models to analyse experimental datasets and uncover structure-function relationships that determine membrane performance. By combining statistical