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, emerging instructional models, and innovative educational practices that strengthen student learning and institutional competitiveness. Recruit, develop, empower, and retain a high-performing faculty while
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, Machine Learning, or related areas. - Knowledge of Large Language Models and Retrieval-Augmented Generation. - Experience or interest in Knowledge Graphs, Semantic Web technologies, information retrieval
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experience-dependent plasticity. Our research combines structural and functional neuroimaging, EEG/MEG, behavioural methods, computational modelling, automated neuroanatomical phenotyping, machine learning and
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. Trains, deploys, and evaluates machine learning models. Uses subject matter and best practices knowledge to perform lab and/or research-related duties and tasks. Works independently to assist with
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disease mechanisms and therapeutic targets, small-molecule and biologic discovery and/or delivery, development of organoid and animal models, and engineering of advanced targeted technologies
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activity using fully compressible MHD in global and local frameworks, integrated with physics‑informed machine learning and coronal/wind modelling. Key tasks and responsibilities: Develop and implement data
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actions, anticipate future behavior, and reason under uncertainty. The methodological scope includes machine learning, computer vision, multimodal perception, probabilistic modeling, and interpretable
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and adapt assimilation schemes based on generative deep learning methods (such as flow matching and diffusion models). The candidate should have previous experience in data assimilation and/or deep
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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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, small-molecule and biologic discovery and/or delivery, development of organoid and animal models, and engineering of advanced targeted technologies for therapeutic use including but not limited to (epi