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patient care. This is a highly applied computational research role, with opportunity to create meaningful clinical impact! General Duties: Develop AI/ML models for analysis of clinical single-cell data Use
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ambitious scientific challenges. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7083-GUIGIN-015/Default.aspx Work Location(s) Number of offers available1Company
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about our benefits and working at SLU by visiting: https://www.slu.se/en/about-slu/work-at-slu/ Location: Uppsala. Form of employment: Temporary employment 24 months, with the possibility of extension
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Assisting with research on the evaluation of low-carbon technologies conducted by the research team, including numerical calculations and analysis, etc., using Excel. [Work content and job description
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Post Doctoral Researcher / Senior Post Doctoral Researcher - School of Biochemistry and Cell Biology
work at the interface of academia and industry, leading research on executing advanced transcriptomics and translatomics analyses to drive ovarian cancer target discovery and commercialization. The role
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Award . Role This position is part of a new Advanced Research + Invention Agency-funded project, subject to contract negotiations, part of the Accelerating Adaptation programme (see https://aria.org.uk
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 6 hours ago
Position Title Post-Doc Research Associate Vacancy ID PDS004970 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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structures of the future tracker. These activities will be carried out at IP2I. The analysis will mainly focus on the search for CP violation in the top quark sector with data from Run 3 of the LHC
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technology development project in which UCC collaborates with two companies, Danalto and Qorvo. The UCC part of the project focuses on the development of agentic AI and digital twin concepts for Ultra-Wideband
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https