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algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. This will be done by combining the mathematical and computational cultures
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by identifying “the right drug for the right patient at the right dose”. The candidate will also work with, and be co-mentored by, exceptional scientists in Human Genetics and Computational Sciences
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the mathematical and computational engine of Artificial Intelligence (AI), and therefore it is a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world
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computational methods and tools, including prior experience with algorithms relevant to computational biology, is a plus. ● Ability to work independently as well as part of an interdisciplinary team in a
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validate predictive algorithms for biomarker discovery Optimize data integration techniques for multi-omics and clinical datasets Perform trend analysis of bacteria-containing samples over time to observe
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an extraordinarily rich, technology-focused network. This includes DARPA program managers and research performers from leading universities, commercial firms, and non-profit R&D organizations. For early-career
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computational biology, computational medicine, statistical genetics, biostatistics, computational chemistry, computer science, physics, statistics, mathematics, or applied mathematics. Substantial coding
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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Details Title Postdoctoral Fellow in Modular Deep Learning School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Science Position Description The Data
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
-year, time limited position with the possibility to extend to 2 years. This is a computational position that offers a collaborative opportunity between basic science (computational genome science) and