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methodological approaches. The selection is done according to the SCRIPTS Diversity Rules & Guidelines: https://www.scripts-berlin.eu/about-us/Diversity/Diversity_Rules_Guidelines/index.html Job description: The
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. You will have evidence of post-qualification research experience, research experience in the analysis of large biomedical or biobank datasets, a proven ability to code and a strong publication record
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, econometrics or another relevant field. You will have substantial relevant research experience in complex trait genetics, a proven ability to code and a strong publication record. If no suitable applicant is
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Development, training and calibration of an educational AI agent for automated annotation, coding and labeling support for transcribed interviews and other qualitative text data. Operationalization and
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letter describing research interests, relevant experience, and career goals Transcript (unofficial is acceptable) Sample code (optional) Sample paper (optional) Shortlisted candidates will be contacted
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discipline *Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant. Expertise in atmospheric physics and chemistry Enthusiasm for coding of complex atmospheric
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: Comprehensive personalised Assessment, early Risk Evaluation and clinical management” (HER-CARE) project. The successful applicant will work on the project “Assessing the role of rare germline non-coding genetic
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Title: Non-Student Research Assistant Employee Classification: Non-Pos Hourly Posting Code Campus: University of North Texas Division: UNT-Provost SubDivision-Department: UNT-College of Information
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 1 day ago
Job Code: Tü_110 Job Offer from March 06, 2026 The Optics and Sensing Laboratory at the Max Planck Institute for Intelligent Systems in Tübingen has an open position for a Researcher to join the
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workshops with carers or stakeholders, and qualitative data coding and content analysis, alongside conducting meta-systematic (umbrella) literature and policy reviews on caregiving experiences. A key aspect