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are seeking applications from individuals with a PhD in any one area of engineering such as: aerospace, chemical, civil and environmental, computer science and software, electrical and computer, industrial and
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data issues. Utilizing machine learning techniques as appropriate for data analysis. Developing computing programs and software to support research initiatives. Applying new methodologies to real-world
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Apply Now Job ID JR101405Date posted 09/12/2024 The Machine Learning Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL) invites exceptional candidates
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machine learning applications in health data analysis. Prior experience in grant writing and funding acquisition. Conditions of Employment Department 3KM100 Global Research Institute Location William & Mary
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the use of R and/or Python Basic understanding of statistical modeling, and machine learning Understanding of high-throughput sequencing techniques including whole genome, whole exome, targeted capture, RNA
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knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required. The ideal candidate should demonstrate a record of publications in the area. Knowledge in
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in the University of Minnesota. The research will focus on applying, developing and implementing novel statistical methods for causal inference, integrative data analysis or/and machine/deep learning
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collaborative setting that emphasized functional genomics and high-dimensional omics data analysis. The lab is a part of the NGI, home to experts in genetics, multi-omics, machine learning, and clinical research
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record, and available funding. Education Requirement: A PhD degree (or about to complete) in machine learning, or a closely related field, is required. Required Qualifications: Practical experience in
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. Applicants with a knowledge base in Vibrio biology would be preferred. Minimum Education and/or Work Experience M.D. or PhD Required Qualifications Ph.D. Certificates/Credentials/Licenses Ph.D. Computer Skills