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Stanford’s Department of Computer Science and the Data Science team at Stanford Health Care. Our research spans both core methodological advancements (e.g., developing novel ML architectures and evaluation
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, treatment planning, and patient care. The research will involve data collection and preprocessing, model architecture design, pre-training and fine-tuning, evaluation and benchmarking, and model optimization
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activities within the areas of embedded software. The position requires a PhD degree within a relevant area (e.g. software, computer, or control engineering) and the desired candidate is expected to have
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mathematics/statistics department at Illinois Tech that offers BS through PhD degrees, including a nationally ranked Master of Data Science in partnership with the Department of Computer Science, and the newly
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require close collaboration with members of the Neutron Technologies Division, the Neutron Scattering Division and the Computer Science and Mathematics Division, as well as project members from other DOE
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familiarity with DMS and DERMS functionalities and architectures; evaluate and enhance operational schemes under high DER penetration. Model microgrids in distribution networks including dynamic and steady
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and computational methods. The lab encourages wet lab scientists to engage in their own data analysis and provides a supportive environment to this end. The group is funded by European Research Council
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the study of the impact of digital and computational pathology on clinical workflows and patient care. Our lab is located in the heart of Munich at the TUM Klinikum rechts der Isar (MRI), Institute
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conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings. Knowledge of systems
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researchers to analyze gene expression patterns within intact tissue architectures. By preserving the spatial information of RNA molecules in biological samples, this technology provides insights into gene