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-driven approach for optimizing the growth of semiconductor materials by combining machine learning with a physics-based understanding of the growth process. Doping and processing of ultra-wide bandgap
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experimental studies, mechanistic modelling, time-resolved data analysis, and machine learning to develop and validate predictive models linking process signals to reaction behaviour, progressing from controlled
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research. Teaching may also be included, but up to no more than 20% of working hours. The position shall include the opportunity for three weeks of training in higher education teaching and learning
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PhD students. The research will be conducted in a collaborative and multidisciplinary environment, with close interaction with major industrial and research partners (e.g., Ericsson, Tele2, RISE
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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includes the opportunity for three weeks of training in higher education teaching and learning. The postdoctoral fellow will: Develop and maintain harmonized satellite time-series datasets (Landsat and
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Institutet our overall aim is to uncover fundamental features of normal and leukemic stem cell biology that can instruct new strategies for clinical surveillance and treatment in hematologic malignancies. Your
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. Conduct transportation resilience assessment and enhancement studies based on GIS, complex network analysis, and machine learning. Simulate human mobility in response to extreme weather events (e.g
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estimators such as Design-based Supervised Learning and Prediction-Powered Inference, and applying mechanistic interpretability techniques, for example sparse feature circuits and the SHIFT method, so that
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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and