13 big-data-machine-learning-phd Postdoctoral research jobs at University of California
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programming skills are essential, along with a track record of published papers and strong self-motivation. - Prior experience in the areas of machine learning / data-driven methods, signal processing and/or
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are essential, along with a track record of published papers and strong self-motivation. - Prior experience in the areas of machine learning / data-driven methods, signal processing and/or applied mathematics
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for Geographic Information and Analysis (NCGIA). Qualifications Basic qualifications (required at time of application) Applicants must have completed all requirements for a PhD Degree in Geography or a related
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. The postdoctoral scholar will have primary responsibility for the collection, quality control, and analysis of single-cell RNAseq (and other ‘omics) data from a diverse panel of diploid and polyploid wheat and
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, particularly on measurements and searches using jet substructure and development of advanced techniques in particle tagging, including applications using machine learning, and are expected to take leading roles
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University of California, Berkeley, Department of Electrical Engineering and Computer Sciences Position ID: University of California, Berkeley -Department of Electrical Engineering and Computer
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management and coding, machine learning AI, and familiarity with LSST data management image reduction and analysis tools. Familiarity with processing data at the DOE US Data Facility at SLAC National
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University of California, Berkeley, Department of Electrical Engineering and Computer Sciences Position ID: University of California, Berkeley -Department of Electrical Engineering and Computer
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Barbara (UCSB) is seeking candidates for a postdoctoral position working on the CMS experiment at the Large Hadron Collider (located at CERN in Geneva, Switzerland). The successful candidate will play a
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linkages between various fisheries and related ecological and social drivers. Climate change focused activities are expected to leverage large climate data sets as well as downscaled climate models