37 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" scholarships in Sweden
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and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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. Previous experience with machine learning applications in molecular modelling, including experience with at least three of the following Python libraries: TensorFlow, PyTorch, JAX, RDKit. Previous
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Python) and data analysis or machine learning applied to materials science Ability to work in interdisciplinary project or industrial experience About the employment The employment is a temporary position
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Computational Mathematics for reliable and trustworthy uncertainty quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a
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well as the programmes in statistics, cognitive science and innovative programming. Read more at https://liu.se/ida The position is based at the Division of Statistics and Machine Learning (STIMA). We conduct research
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communications theory methods Applying optimization techniques and machine learning/AI approaches Conducting simulations and experimental validations Collaborating with Ericsson and Chalmers researchers Publishing
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access to preventive care and neighborhood characteristics influence long-term health trajectories. The project applies both econometric and machine learning approaches to identify high-risk groups and to
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world. We look forward to receiving your application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is
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of extensive datasets. You will be supervised researchers who collectively offer expertise in computational biology, genetics, epidemiology, and machine learning. The research will be closely linked