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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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basic technologies, computer vision, image understanding, and other multi-media sensing and recognition techniques are widely studied. In addition, machine learning including deep neural networks
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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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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
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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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and ML pipelines for drug synergy, write code for data analysis and post-processing data. Training of models like CNN, RNN, Transformers with some work in classical machine learning with XGBDTs is
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projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological
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are looking for a candidate with a background in machine learning or data science and strong software engineering skills. The Ph.D. position is a part of the strategic research area in IT and mobile
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Research Scientists as part of its new initiative, Polymathic AI, Building Foundation Models for Science. Recent advances in machine learning, including Large Language Models and diffusion based generative