856 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "The University of Gothenburg" positions at University of Colorado
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Scientist, Organoid Initiative Position # 00844419 – Requisition #: 38704 Job Summary: We are recruiting a Leading Scientist focused on automation, workflow engineering, and data integration. This individual
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engagement metrics and recommend data-driven improvements. Coordinate logistics, budgets, vendor relationships, and compliance requirements for on- and off-campus events. Ensure alignment with University
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the main point of contact with the LIS vendor for system support and maintenance. Other laboratory computer applications may be included in the scope of work as the position evolves. Key Responsibilities
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experience Preferred Qualifications Bachelor’s degree in science or health related field Three (3) years of clinical research or related experience Experience with electronic data capture systems (e.g. EMR
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trials and studies Obtain study subject’s medical history and current medication information, reviews research protocol inclusion/exclusion criteria, and confirms eligibility of subject to participate in
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). This position’s appointment has a limited duration of one (1) year from date of hire. Key Responsibilities: Data collection and management of a departmental prospective database. Perform relevant literature reviews
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electronic data capture systems (e.g. EMR or EHR and data management systems) · Competencies, Knowledge, Skills, and Abilities · Advanced knowledge and understanding of federal regulations and
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attend their meetings as assigned. Participate in training of other AHP outreach coordinators as new hires are onboarded. Enroll study subjects and collect data (from patients and/or health care providers
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, while contributing to the strategic growth and success of departmental goals. Key Responsibilities: Financial Analysis, Accounting & Reporting – 40% Analyze financial data to identify trends, variances
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symptoms appear. We’re seeking a Data Scientist who can analyze real‑world datasets and build well‑documented, reproducible machine‑learning pipelines in Python. You will drive exploratory data analysis