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. Experience with deep learning and programming, preferably in Python, are required and should be evident from your academic track record, including the (online) courses you've followed, your publications
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deep learning models using the Oak Ridge Leadership Computing Facility (OLCF) systems. Conduct research with scalable transformer-based foundation models with large volumes of spatiotemporal physical
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. in Computer Science or a closely related field, with research focus in Reinforcement Learning, Machine Learning, Embodied AI, or AI for Education. Strong background in deep reinforcement learning, with
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or deep learning reconstructions). Knowledge of radial data acquisition strategies, artifact mitigation methods, and their use in parametric imaging (e.g., T1/T2/T2* mapping). Preferred Qualifications
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, machine learning, and AI applications in radiology. The research area includes innovative work on developing Deep Learning Based Image reconstruction in CT on Photon Counting Detector CT with work in
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their valuable contributions and deep connection to country and pay respect to Elders past and present. Advance internationally recognised Antarctic climate and palaeoclimate research Be part of nationally funded
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developing new ones, including machine and deep learning methods. There will be opportunities for development of new cell line and animal models and testing, evaluation, and analysis of new genomic
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, implement, and analyze assessments of student learning, affect, and experiences. - Disseminate research outcomes through peer-reviewed publications, conferences, and seminars. - Pursue extramural funding
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(e.g. use of OpenRouter, vLLM). Experience training LLMs and deep learning in general is a plus; good communication skills in oral and written English. Contributions to OpenML or other dissemination
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heterogeneity in cancer, inflammation, and tissue senescence. • Developing next-generation deep-learning and statistical deconvolution methods for inferring gene regulation from bulk, single-cell, and spatial