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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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skills (e.g. Python, Julia) to merge concepts of chemical engineering, operations research and computer science, as you may also need to deploy machine learning to support data analytics and complex
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communities bordering the West Nile, Lake Albert, and Lake Victoria. To be considered for the role, you should hold (or be close to completion of) a PhD/DPhil in Health Data Science, along with relevant
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., knowledge representation and reasoning) and bottom-up (e.g., machine learning) methods to study the representation of geographic categories and processes. While we welcome applicants from a broad range of
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, working with some of the world’s largest single-cell data assets, and in the context of the world’s largest longitudinal population studies, many hosted here at the Big Data Institute, as well as other
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groups working on digital health and wellbeing , network science , computational social science , and various topics in machine learning. You will be working in the research group of one of the PIs
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together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is
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computing, computer architecture, programming models and high performance computing. These are your qualifications: Must-haves: Completed doctoral/PhD studies in Computer Science or a closely related field
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following: Quantum chemistry (preferably of excited states) Multiscale simulations/environmental modelling Excited state dynamics Data Science/Machine learning in chemistry/Cheminformatics Molecular dynamics
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at Université Paris-Saclay (https://cvn.centralesupelec.fr/ ), Prof. Pock from the Institute of Computer Graphics and Vision at Graz University of Technology (ICG ), Prof. Thiran from the EPFL Signal Processing