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Field
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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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; Familiarity with machine learning concepts and large language models. Deep expertise is not required, but candidates should be comfortable engaging with these technologies at a foundational level; Knowledge of
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strong technical expertise in deep learning, such as models for image segmentation, classification, multi-modal processing, foundation models, or agentic frameworks. An extensive background in computer
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monitoring systems, bedside monitoring devices, or medical device data. Experience linking physiologic waveform features to clinical outcomes. Experience with machine learning, deep learning, predictive
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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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skills in English (written and spoken). Programming skills and experience with deep learning frameworks (PyTorch, Hugging Face, etc.). Qualification requirements The eligibility criteria for prospective
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are especially encouraged: (1) statistical mechanics, condensed-matter theory, quantum field theory; (2) tensor networks, quantum information, quantum algorithms; (3) machine learning, generative models, large
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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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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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Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job Research Description The project aims at combining three primary data sets (JWST NEXUS spectroscopy, Euclid Deep Field North