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Field
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qualification in Genetics, Bioinformatics, Computer science, Data science, Statistical Genomics or a related discipline involving the interrogation of ‘omics’ datasets. Hands-on experience with large-scale human
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Full-Time, Fixed-Term (36 months) Salary is £41,945 to £48,003 pro-rata per annum (including London Allowance) We are looking for a highly motivated early career researcher with a PhD (or near
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-of-the-art sparse algorithm in matrices, tensor and networks for large-scale numerical, scientific and AI models and disseminating findings through publications and presentations in top-tier peer-reviewed
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experience in life cycle assessment (LCA) and related tools for managing large data sets to evaluate natural resources needed to advance emerging technologies. The candidate will lead their primary project and
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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | about 1 month ago
of (a) (young) stellar clusters, (b) molecular gas and dust, or (c) star-forming sites (e.g. HII regions) in gas-rich galaxy centers. The work will encompass all aspects from data reduction to their in
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access to many of the College’s facilities including the gym and pool Free fitness and wellness classes And more! https://www.bowdoin.edu/hr/benefits-perks/index.html Education/Skills Requirements PhD
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, including fitting and manipulation of large array-type data sets (using Python, Matlab or equivalent) Ability to communicate well, and work within a collaborative team environment Preferred Knowledge, Skills
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available within the Department of Medicine, Division of Endocrinology in the Laboratory of Brain Behavior and Big Data at the University of Southern California. The applicant with work with PI and
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you will be offered an 18 month fixed term contract. About you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified
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The candidate will have a PhD or equivalent degree in bioinformatics, biostatistics, computational biology, machine learning, or related subject areas Prior experience in large-scale data processing and