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bioinformatics, computational biology, genomics, statistical genetics, or a related quantitative field, together with demonstrated expertise in large-scale genomic data analysis and significant experience in
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reviews Lead the analysis of large-scale population level data sets using statistical and data science techniques Provide statistical and mathematical modelling advice to junior researchers Lead the writing
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of this programme. The profile PhD in computer vision, computational biology, physics or a related discipline Demonstrated expertise in image analysis and working with large-scale imaging datasets Strong expertise in
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, clean, and analyze large-scale datasets combining financial, environmental, and policy information, including both structured and unstructured data sources. Apply artificial intelligence and large
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) Location: Big Rapids, MI Department: OAR Research Support Advertised Salary: $14.25 Summary of Position: The Summer Student Fellowship is intended to promote collaborative research projects between faculty
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Bachelor’s degree or above (for Research Assistant I) in psychology, data science, computer science, or a related discipline. The appointee should demonstrate strong quantitative and computational skills, with
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comprehensive excel spreadsheets to track progress; comprehensive data management as required by a large-scale RCT; liaisng with the trial steering committee; contributing to and leading on reports to the funder
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mentoring junior colleagues Desirable criteria Experience in handling complex data from large observational dermatology studies Evidence of contribution towards developing and submitting research funding
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Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. NTU’s College of Computing and Data Science (CCDS) is a
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endemic countries. We are seeking to appoint a Research Fellow to join a research programme that applies advanced bioinformatic, statistical, and population genomic approaches to large-scale sequencing data