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Field
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learning environments that are free from harassment and prohibited discrimination. Duke prohibits discrimination and harassment in the administration of both its employment and educational policies. Duke
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Computational complexity in adiabatic quantum computation AI to boost quantum technologies (e.g., machine learning for quantum error prevention/mitigation/correction) Quantum machine learning Quantum cloud
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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the development of high-fidelity finite element models to investigate surface wave propagation in soft biological tissues, forming the foundation for subsequent statistical and machine learning
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parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing; additionally other methods such as simulation-based inference Good computing skills
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To undertake research on the evolution of prokaryotic pangenomes using machine learning and AI approaches. The work will involve the analysis of large prokaryotic genome datasets, the development
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FieldComputer science » OtherEducation LevelPhD or equivalent Skills/Qualifications CANDIDATE ’S PROFILE The candidate should possess a PhD in machine learning or computer vision and have a strong publication
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. Experience with machine learning is highly preferred. Ability to work independently and as part of a team. Key Requirements for PhD: Hold a Bachelor's degree with outstanding performance in Physics or related
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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Stability Market, located in Luxembourg. The candidate will join the Security, Reasoning and Validation (Serval) research group and work on a research project related to the application of machine learning