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Field
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. The department has a strong community on related topics: research groups working on digital health and wellbeing , network science , computational social science , and various topics in machine learning. You will
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for FLASH-RT: strategies to obtain the shortest delivery time on healthy tissues and high dose-rate coverage of large volumes need to be evaluated for the current machine settings, high spatial and temporal
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and studying the phenological migration behavior of small insects using the terra-mobile platform on the Salter Research Farm. Analyze data and summarize results by writing scientific papers
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental
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datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep learning architectures including generative models, particularly
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together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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visual representation and analysis of large-scale 2D/3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems Section, Computer Science and Mathematics Division
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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knowledge of statistical techniques for data analysis Experience in detector performance or trigger systems for high energy or nuclear physics experiments Experience with machine learning techniques and tools