Research Assistant and PhD fellow in the Genetic architecture of skeletal muscle

Overview Research Assistant and PhD fellow in the Genetic Architecture of Skeletal Muscle. The PhD fellowship is part of the CBMR International PhD & Postdoc Program and will commence on 15 November 2026 or after agreement. Project Description Body composition plays a central role in cardiometabolic health. While large‑scale genetic studies have improved our understanding of general adiposity, the biological determinants of skeletal muscle traits at population scale remain incompletely understood. Leveraging MRI data from the UK Biobank, this project aims to study skeletal muscle traits at scale and identify genetic factors influencing variation in skeletal muscle composition. The Project Will Generate robust imaging‑derived measures related to skeletal muscle using population‑based MRI data. Identify genetic variants associated with these skeletal muscle traits. Explore the relevance of genetic findings for metabolic health and disease risk using genetic and epidemiological approaches. Your role Process and quality‑control large‑scale abdominal MRI data. Develop reproducible pipelines for skeletal muscle phenotyping. Perform genome‑wide and gene‑level genetic association analyses. Integrate genetic findings with proteomic and functional genomic data. Interpret results in close collaboration with clinical and experimental partners. Disseminate findings through publications, conference presentations, and open, well‑documented analysis workflows. Principal supervisor Professor Ruth Loos, View email address on click.appcast.io Start and Duration Start: 15 November 2026. Duration: One year as Research Assistant followed by three years as PhD fellow contingent on a positive evaluation of the first year. Profile & Required Qualifications Early‑career researchers with strong quantitative training who aspire to a career in academic research at the interface of imaging, genetics, and metabolic health. Master’s degree in Biomedical Sciences, Bioinformatics, Computational Biology, Medical Imaging, Biostatistics, or related fields (equivalent to a Danish Master’s). The degree must be obtained before 15 November 2026. Strong quantitative and analytical skills. Experience working with large‑scale biomedical datasets. Programming skills in Python and/or R. Familiarity with Linux and HPC environments. Curious mindset with a strong interest in imaging‑based phenotyping, statistical genetics, and metabolic health. Excellent English communication skills, written and oral. Experience with MRI analysis, deep learning, statistical genetics, or multi‑omics integration is advantageous but not required. Eligibility Applicants must hold a Master’s degree equivalent to a Danish master’s before 15 November 2026. Applicants are not eligible if they have been employed in a Research Assistant position for more than two years at the University of Copenhagen prior to the commencement of the fellowship. Terms of Employment The PhD fellowship is contingent on successful enrolment as a PhD student at the Graduate School at the Faculty of Health and Medical Sciences, University of Copenhagen. Salary, pension and terms of employment are in accordance with the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State. Employment as a PhD fellow is subject to background screening in accordance with the University’s security policy. EEO Statement The University of Copenhagen reflects the diversity of society and welcomes applications from all qualified candidates regardless of personal backgrounds. #J-18808-Ljbffr Department of Pharmacy, University of Copenhagen