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. The successful candidate will play a key role in designing and implementing research at the intersection of sensor data collection, machine learning, remote sensing, and real-time agricultural decision-making
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interpretable machine-learning models, including approaches for uncertainty quantification, model calibration, and transferability. The Postdoc will also contribute to the development of reproducible
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states, tissue organization, and biological programs, and connect these representations to mechanisms of human disease. Your work will sit at the intersection of machine learning, computational biology
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the department and the funding agencies. The successful candidate will join the Machine Learning & Data Science research group and conduct research on the mathematical theory of deep learning as part of the joint
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genomics, population genetics, and machine learning. The successful candidate will contribute to method development, software dissemination, and scholarly publications. Duties and Responsibilities Lead
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an established code (for example VASP, Quantum ESPRESSO). ● Research experience performing molecular dynamics simulations. ● Experience training, fine-tuning, or validating machine-learning interatomic
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Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
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machine learning, topological quantum materials, and/or related areas. The successful candidate will have access to HiPerGator, the fastest university-owned supercomputer in the United States (TOP500
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assignment location. Be Bold. We are recruiting postdoctoral fellows in the following research areas: Regenerative immunotherapy and CAR T-based approaches for tissue repair. The postdoctoral fellow will
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, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral, and interpersonal