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work rather than only contributed to it. Fluency in at least one scientific programming language (R, Python, MATLAB) and demonstrated experience assembling or analyzing large or heterogeneous datasets
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Boehringer Ingelheim International GmbH | Biberach an der Riß, Baden-Württemberg | Germany | about 6 hours ago
functional validation. Proficiency in scientific programming and quantitative data analysis. Ability to formulate testable hypotheses and develop a realistic, innovative research plan suitable for
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Python and R programming for statistical modeling, data visualization, and integration of large-scale genomics datasets Participate in the design of strategic data acquisition plans to support ongoing
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. Co-AIMS offers a unique opportunity to drive UK leadership in human-centred, ethical AI for manufacturing. https://www.birmingham.ac.uk/news/2025/new-research-hub-could-revolutionise-ai-based
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physics, as well as the team’s work on applied mathematics and advanced numerics, via the ACT website ( https://www.esa.int/gsp/ACT/) . You also are encouraged to visit the ESA website: https
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management record. Preferred Education and/or Experience: Candidates with extensive research experience in computer programming and data management will be given preference. Candidates with successful grant
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faculty (non-tenure track Assistant Professor) to join the Cleveland Clinic Genome Center ( https://www.lerner.ccf.org/genomics-research/ ) directed by Dr. Feixiong Cheng at the Cleveland Clinic Research
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) Demonstrated programming skills in C/C++, Python, R or comparable languages. Experience with AI-based coding tools such as Claude Code, Codex or Cursor. Evidence of research productivity through publications
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integration, etc.. Participation in these projects will include scientific programming, data analysis, manuscript preparation, meeting with collaborators, etc.. Minimum Qualifications A Ph.D. in computer
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survivability and in network optimization. Solid programming skills (for example in Python) and hands on experience with mathematical optimization and network modelling. A good working understanding of