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highly desirable. Proficiency in programming languages such as Python or C++. Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch) and advanced data analysis tools. Excellent problem
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Learning (ML), Deep Learning (DL) particularly in Natural Language Processing (NLP) and Computer Vision (CV) - strong record of publications - Excellent communication skills and ability to work in a fast
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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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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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at the atomic scale, using density functional theory-accurate machine-learned potentials and molecular dynamics simulations, in close collaboration with leading European research institutes and steel industry
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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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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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: ● Applicants must have a PhD in Computer Science or related field, with no more than five years post receipt of the PhD. ● Experience in one or more ML domains, such as deep learning, reinforcement learning