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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
Position Title Post-Doc Research Associate Vacancy ID PDS004788 Full-time/Part-time Full-Time Temporary Hours per week 40 FTE 1 Work Location Chapel Hill, NC Position Location North Carolina, US Hiring Range
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. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
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candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning
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techniques. Join our team and be involved with writing up data from all of these great and innovative multimodal studies. The Post-Doc will provide technical, analytic, and administrative assistance supporting
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fundamentals of complex matter, and (iii) new technologies to envision applications of molecular machines in the real world. The Post-doctoral associate will be based in Strasbourg at the Institut Charles Sadron
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and quantitative gene expression analysis Excellent organizational, communication, and teamwork skills Certificates/Credentials/Licenses Computer Skills General office suite and willingness to learn lab
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Full Time Posting Number 26FA0157 Posting Open Date Posting Close Date 08/31/2026 Qualifications Minimum Education and Experience PhD, MD, or equivalent doctoral degree in neuroscience, biostatistics
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and patient-reported outcomes; (b) observational research and comparative effectiveness studies; (c) intervention studies; (d) clinical informatics, mobile/electronic health; (e) machine learning
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data