21 computer-vision-and-machine-learning "https:" Fellowship positions in United States
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Challenge grant. The successful candidate will work closely with the Principal Investigators (PIs) to develop and implement innovative research integrating machine learning, computer vision, and wildlife
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processing, and sensor integration. Strong background in signal processing, computer vision, or machine learning. Proficient in programming languages such as Python, C++, and MATLAB. Strong publication record
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pathophysiological mechanisms for acute organ injury research under POI-KB. · Authorship of high-impact first-author or co-author manuscripts in leading informatics and machine learning journals and conferences
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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, including but not limited to deep learning, computer vision, computational linguistics, pretraining methods, interpretability, and transfer learning Experience with cognitive science, particularly
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Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), particularly in Natural Language Processing (NLP) and Computer Vision (CV) Familiarity with genomic and bioinformatic databases
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in machine learning and/or computer vision as applied to robotics. - Strong publication record and demonstrated research independence. The referenced salary range is based on Johns Hopkins University's
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technical depth in NLP methods and a clear interest in problems related to AI safety. Minimum Qualifications: PhD in Computer Science, Information Science, Computational Linguistics, Machine Learning, or a
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experience applying computational and laboratory techniques to agricultural research questions related to crop resilience, plant health, and food security. Learn more about the research being conducted
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retirement program with a generous employer contribution and additional voluntary retirement programs (457 or 403b) are available. https://www.kumc.edu/human-resources/benefits.html Employee Type: Regular Time