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sustainability outcomes. Your primary tasks will be to: Explore, develop and evaluate different machine-learning methods for defining urban mobility culture typologies Big Data analysis and visualizations Collect
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of good-quality data is typically limited for high-value critical assets. This PhD project will focus on developing, evaluating, and demonstrating physics-informed machine learning or domain knowledge
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nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory
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bioinformatics and modeling of the complex microbial communities that shape our world. We are seeking a highly motivated PhD student to develop scalable probabilistic machine learning models and algorithms
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15th June 2025 Languages English English English What can microbial DNA tell us about the environment? Join us discovering this with AI! PhD scholarship within bioinformatics, using data science in
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in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages, PyTorch Familiar with foundation models (vision large models or multi
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Vacancies PhD position Sensor based modelling of social and physical activity in high schools Key takeaways In this PhD research, you will specifically focus on the design of machine learning and
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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Human Genome-Phenome Archive. The position will also be connected to a vibrant local ecosystem for data science and machine learning. Your Tasks The research group of Dr. Brian Clarke is looking for a
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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g