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- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Delft University of Technology (TU Delft); Published yesterday
- Delft University of Technology (TU Delft); today published
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description Positive tipping points
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in Computer Science, Artificial Intelligence, or related field. Solid programming and development skills (Python, Git, Bash). Experience with machine learning (e.g PyTorch/TensorFlow). Strong interest
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analysis, has good software skills (Python, C++, ROOT) and has (some) research experience in experimental particle physics. Experience with machine learning algorithms and software is desirable but not
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has been studying any of these topics: statistical physics, computer simulation methods, and polymer physics. Proficiency in the C++ and/or Python programming language is an advantage. Good knowledge
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opportunities for collaboration with others and learning about a wide variety of topics in quantum computing and quantum information theory. What are you going to do? You are expected to: carry out original
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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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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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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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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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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