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Apply now The Faculty of Science, Leiden Institute of Advanced Computer Science,is looking for a: PhD Candidate Human-Centered Interpretable Machine Learning (1.0fte) Project description In recent
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enhance real-time decision-making in road traffic management. The project aims to bridge the gap between recent advances in AI and machine learning, in particular, multimodal and instruction-tuned
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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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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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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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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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, 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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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