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statistical approaches. A fundamental understanding of Deep Neural Networks as applied to high-frequency time series datasets, including the ability to design and implement custom NN models in PyTorch, as
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-doc Fellows in Cancer Prevention and Control Minimum Qualifications Ph.D. in Nursing, Public Health, Psychology or health-related field by the time of hire. Preferred Qualifications Superior ability
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of neural activity with control of defined circuit elements and utilizing imaging, optogenetics, anatomy, high-density freely moving neurophysiology, behavior, and computation. Ideal candidates will have a
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with basic Immunological concepts. Preferred Qualifications Prior experience with live cell fluorescent imaging. Experience working with Python, R, and/or command line applications. FLSA Exempt Full Time
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parity-check codes and iterative decoders, investigating applications of quantum error correction in quantum computers and networks. We are interested in candidates with strong expertise in burst error
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for growing bioenergy–sorghum focused. The role combines gene network modeling with ecophysiological modeling, integrating a wide variety of phenotypic data to improve bioenergy cultivation practices
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. Additional duties as assigned. Knowledge, Skills and Abilities: Knowledge in scientific or engineering field related to Artificial Intelligence and Machine Learning. Knowledge in control theory and
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nuclear systems and neutrino dynamics. Qutrit-based and hybrid quantum-classical approaches for many-body simulations. Tensor network and entanglement-based methods in many-body physics. Knowledge, Skills
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implementing AI tools, to analyze omics data on a high-computing cluster. Participates in data interpretation such as data quality control, variant interpretation and classification. Utilize good coding
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of only 53 in the nation. The UACC Cancer Prevention and Control Program is seeking a talented postdoctoral trainee to join their team. The successful candidate will conduct innovative research examining