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– including the health, safety and wellbeing of workers. The aim of the research programme ALGOSH: Algorithmic management at work - challenges, opportunities and strategies for occupational safety and health
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passive and active flow control algorithms, potentially incorporating machine learning/AI, to enhance aerodynamic performance and stall delay with rapid response times. The research is conducted in
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include the design and implementation of finite element multiscale models and machine learning algorithms, analyzing related experimental data, and collaborating with industrial collaborators to validate
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these questions, we will determine RNA structures in vivo using cutting-edge transcriptome-wide RNA structure probing techniques that together with computational models and machine learning algorithms will generate
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driving range. The project aims to analyze algorithms for predicting the remaining driving range of EVs and suggest ways to improve the current state of the art. The idea is to develop a model-based
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Scientific Computing the research and education has a unique breadth, with large activities in classical scientific computing areas such as mathematical modeling, development and analysis of algorithms
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synchrotron radiation methods. Experience in programming, data analysis, and algorithm development. (MATLAB, Python, C++, etc.) Experience in developing simulations related to X-ray characterization Track
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the opportunity to develop your own research ideas within the lab’s focus areas Build and refine computational models of human innovation and learning processes Design and test AI algorithms
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. The successful candidate will work on cutting-edge projects involving artificial intelligence (AI) and computational pathology, with a particular focus on developing and applying machine learning algorithms
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their research agenda in close collaboration with their supervisor. Potential areas of interest include, but are not limited to: Efficient and scalable algorithms for machine learning, Optimization problems with