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: Applied mathematics; Machine Learning; Mathematical Modelling Appl Deadline: 2026/03/24 10:59 PM UnitedKingdomTime (posted 2026/03/18 04:00 AM UnitedKingdomTime, listed until 2026/04/01 04:59 AM
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of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and adaptation of existing approaches for scientific applications
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learning or multi-agent systems. Experience with cloud-native technologies (Docker, Kubernetes) or distributed computing. Experience with efficient neural architectures, scalable model design, or resource
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Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven
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are looking for candidates with: An interest in developing mathematical methods and machine learning models good communication skills with sufficient proficiency in oral and written English, an excellent
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demonstrated experience in computer vision or analysis of pathology images. The appointees will participate in a multidisciplinary collaborative research project related to development of deep learning model
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based on machine learning tools for energy problems related to prediction. The application domains include both industry and climate changes. The first two months will be devoted to the study of
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Associate in Research The role involves developing and optimizing machine learning models to predict infectious diseases using multimodal health data. Responsibilities include analyzing correlations between
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regimes; and machine learning, capturing complex nonlinear behaviour at the cost of model opacity. BENEFIT synthesises these paradigms by integrating stability analysis directly into machine learning
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications