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Field
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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Experience Strong background in machine learning, deep learning, or related AI techniques. Proficiency in Python and at least one major deep learning framework (e.g., PyTorch, TensorFlow). Evidence of research
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
environments. To achieve this, novel alignment and editing techniques are required. Specifically, post-training with Reinforcement Learning (RL) presents a highly promising methodology to overcome
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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
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field Demonstrated expertise in one or more of the following areas: Machine/deep learning, artificial intelligence, statistical modeling, or computational modeling Human neuroimaging analysis, including
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completed) in Natural Language Processing or a closely related area. Solid knowledge of machine learning, especially deep learning. Experience in model development and/or fine-tuning. A practical mindset
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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Proven experience with deep learning frameworks such as PyTorch, and familiarity with multimodal data fusion is highly desirable Ability to work independently, as well as collaboratively in
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deep expertise in modern machine learning and a strong record of research accomplishment who are excited to advance foundation models, agentic systems, and new AI approaches for high-impact scientific