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, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong programming skills (Python/C++); Proven interest in generative models
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Would you like to work at the intersection of transportation, robotics and machine learning to design mixed fixed-flexible transport networks? Job description The increase of public transport usage
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theory, discrete optimization and machine learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts
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the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools. The goal is to extract governing equations directly from
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experimental data to test hypotheses or measure phenomena, in online, lab and /or field settings. Identifying the critical assumptions needed to draw inferences from empirical results. Writing computer code to
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approaches that integrate both qualitative and quantitative research (e.g., combining big data or machine learning with in-depth fieldwork) can also be pursued. Method selection and mastery are viewed as part
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based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored, and the findings of your project will be
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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crucial; Design interventions to reduce bias and improve fairness and safety in human-AI interaction. The research will combine computational modeling (e.g., NLP, machine learning, deep learning) with human
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emotion safety is crucial; Design interventions to reduce bias and improve fairness and safety in human-AI interaction. The research will combine computational modeling (e.g., NLP, machine learning, deep