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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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research focus will include some of the following topics: Advanced sensor fusion and multimodal AI models for robotic intercropping. Self-supervised learning will generate multimodal agricultural pre-trained
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sensors, communicating over networks, to achieve complex functionalities, at both slow and fast timeframes, and at different safety criticalities. Future connectivity of the next generation of multiple
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opportunities for embedding storage at multiple points within the HVDC architecture—on the AC side, DC side, or directly within converter submodules. The research will tackle several key technical, economic, and
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model due to the mathematical challenge of solving the multiple partial differential equations simultaneously. With the support of the combined sponsorship from the university and industrial partner
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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), mouse models, fluorescence microscopy (confocal), genomics, epigenetics, cell biology and biochemistry techniques (e.g., RNA/ChIP-seq, mass spectrometry). Successful track record in conducting and driving
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expertise and supervision of experienced researchers from multiple institutes at Forschungszentrum Jülich. As one of Europe’s largest and most multidisciplinary research centers, Forschungszentrum Jülich
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-time 65%) in the DFG-funded Integrated Research Training Group (RTG) Beyond Amphiphilicity – RTG 2670: Self-Organization of Soft Matter via Multiple Noncovalent Interactions . The position is funded from
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TV-L, part-time 65%) in the DFG-funded Integrated Research Training Group (RTG) Beyond Amphiphilicity – RTG 2670: Self-Organization of Soft Matter via Multiple Noncovalent Interactions . The position