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initiative that brings together museum-based experts in marine annelids with specialists in genomics and bioinformatics. Its overarching aim is to unlock Europe’s hidden biodiversity by applying state
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culture. Analyze molecular, physiological, and histological data and interpret results. Perform advanced bioinformatics analysis, employing cutting-edge bioinformatics tools. Help train and mentor students
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-hyperaccumulating plant species across various environments. Analyze and integrate large-scale biological datasets using statistical and bioinformatics tools to support data interpretation and hypothesis development
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publication record. Previous research experience in RNA biology and bioinformatics is preferred. The initial appointment will be made on a fixed-term contract of two years. Re-appointment thereafter will be
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collaboration with our bioinformatics unit) as well as apply advanced approaches and cutting-edge techniques for analysis of mouse models of diseases, primary tissue culture and human samples; including
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fellow with strong expertise in bioinformatics and statistics to join our research team in the Departments of Ophthalmology, Cell & Developmental Biology, University of Michigan. Our research encompasses a
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computational biology, statistical genetics, epidemiology, bioinformatics, or a closely related discipline. Strong programming skills in R, Python, MATLAB, or similar languages are required, along with a solid
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(bioinformatics, genomics, …) and research experience in the field of cancer genomics, although we welcome applications from candidates with diverse educational backgrounds, including computational biology, physics
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, bioinformatics or related disciplines. They should be self-driven, highly motivated, creative with excellent communication skills in written and spoken English and Cantonese. Expertise and knowledge in
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or publications. Qualifications All required qualifications must be documented on application materials. Required Qualifications PhD in biological science and/or bioinformatics or computational biology. Knowledge