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of models like CNN, RNN, Transformers with some work in classical machine learning with XGBDTs is expected. Relevant work can lead to co-author publications and contributions to grant proposals. Tentative
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experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in particular simulation-based inference), strong programming
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/drc/ ). About the role The role will contribute to on-going research at the UCL Hawkes Institute to develop advances in computational modelling of neurodegenerative disease, machine learning, and big
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the development of realtime motion adaptation during treatment on the MR-linac and conventional linac platforms, the development and clinical use of predictive models using machine learning/AI for treatment
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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the development, training, and validation of computer vision models is comfortable working in multidisciplinary research environments, where methods are applied to biological or real-world problems Qualifications
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UPOs PhD enrolment: Université Paris Cité DC15: Hybrid machine learning models for data-driven bioprocess optimisation PhD enrolment: University of Padua Eligibility Requirements: Doctoral Candidates
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP
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descriptors to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and
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Faculty of Life Sciences - Department of Biology Research fellow (m/f/d) in the field of Machine learning and Biomedicine with expected full-time employment - E 13 TV-L HU (third-party funding limited until