258 programming-"Multiple"-"Duke-University"-"U"-"U.S"-"O.P" positions at Nature Careers
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problem-solving service-oriented mindset Ability to articulate clearly technical needs within an interdisciplinary setting Demonstrable computer programming skills are an advantage No prior knowledge
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-performance training/inference systems, your contributions will be critical to our mission. We are hiring for multiple specializations within this role, and we encourage candidates with a deep passion for any
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for scientific work strong programming skills and experience in software projects excellent written and spoken English skills TUD strives to employ more women in academia and research. We therefore expressly
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6000 postgraduates. Our academic staff are drawn from the world’s finest institutions. Here English is the medium of instruction and undergraduate programs are carefully articulated with postgraduate
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computer science at QM. We also offer the opportunity to shape our educational programme to benefit from new developments in pedagogical approaches and content. We expect The candidates must possess research
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edit checks and listings/reports/tools for data review and discrepancy management activities. Support the development of Data Management Plan documents that will ensure delivery of accurate, timely
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Science, Natural Science and Geography. The teaching includes responsibility for a bachelor's programme, a master's programme, single subject courses, and courses in the Primary School Teacher Programme
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learning programme to foster our staff’s soft and technical skills Multicultural and international work environment with more than 50 nationalities represented in our workforce Diverse and inclusive work
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act as the museum's liaison with relevant Danish natural history communities. The successful candidate will be expected to establish and develop an externally funded collection-based research program
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partners. Main Duties Improve, develop, implement, and apply advanced computational tools and workflows to process, analyse, and interpret large-scale LCMS-based metabolomics datasets across multiple species