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Doctoral Candidate in computer vision and machine learning for developing novel deep learning method
Machine Learning (DM3L) Doctoral Candidate in computer vision and machine learning for developing novel deep learning methods for satellite-based tracking of global CO2 and NOX emissions of point sources 80
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guidance and robotics. Our work combines medical imaging, computer vision, and machine learning with strong clinical translation, in close collaboration with Balgrist University Hospital and the national
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engineering, data science, or related fields Strong programming skills (especially in Python), and experience with simulation, modelling, data analysis, machine learning, and hardware control Solid
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data, machine learning models, and data pipelines for real-time and offline analytics. You will help develop and apply digital twins for power grids by integrating physics-based models (e.g., power flow
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also influenced by the intestinal microbiome and its metabolite, especially in the cancer treatment areas such as immune checkpoint blockade or CAR-T cell therapy. The Research Division “Microbiome and
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therapeutic antibodies (fragments) and novel concepts for controlling the function of CAR molecules in patients as well as with structure-function relationships of metalloproteins. Glycobiology projects focus
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therapeutic antibodies (fragments) and novel concepts for controlling the function of CAR molecules in patients as well as with structure-function relationships of metalloproteins. Glycobiology projects focus
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stochastic modelling or other established approaches in economics under uncertainty. Readiness or experience in machine learning and other AI methods is a benefit. In addition, the research professor would