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position with experience in physiologic signal processing (e.g., EEG) and statistical/machine learning methods. The applicant would be joining the lab of Dr. Jennifer A. Kim MD-PhD, which focuses
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team has a strong focus on machine learning and interpretable deep learning. However, more theoretically oriented candidates, interested in developing mathematical and mechanistic models of the immune
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research background; 4) excellent data and statistical skills; and 5) experience with digital technology and knowledge of machine learning, artificial intelligence, and advanced statistical modeling strongly
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Postdoc opportunity: machine learning methods for high-content image screens (Verhaak lab) Description The Verhaak lab is looking to recruit a postdoctoral associate who will help us develop
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: combining machine learning and mechanistic modeling, multisimulation (interoperation with other tools), high-resolution simulations of subcellular chemical dynamics, and algorithm development (GPUs
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to facilitate timely and professional deliverables Experience with causal inference, machine learning, and artificial intelligence is desirable Organization: Yale School of Public Health Department: Biostatistics
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or more of the following is desired: machine learning methods, statistical genetics, large-scale genomic analyses, cloud computing, and single-cell and or bulk sequencing analysis. The ideal candidate will
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interest in algorithms, blockchains and cryptocurrency, causal inference, game theory, learning, machine learning, market design, and networks, but all subjects at the intersection of these three scientific
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: The successful candidates are expected to work on the development and application of bioinformatic (integrative multi-omic methods) and/or computational mass spectrometry workflows (machine- and deep-learning
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expertise Fluent English in speech and writing Effective team player SKILLS PREFERRED Knowledge in one or more of the areas of machine learning, deep learning, distributed computing, p2p networks