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physiology Coding proficiency (e.g., Python, R) and familiarity with deep learning tools are strong assets A strong publication record, critical thinking, and collaborative mindset Application Instructions
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learning and advanced statistical modeling * Proficient programming skills in Python and/or R, experience with machine learning and omics analysis libraries (e.g., scikit-learn, TensorFlow/PyTorch
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science, drug discovery, or related fields are encouraged to apply. Strong computational backgrounds, proficiency in at least one programming language (e.g., R, Python, Perl, C++, Java) and knowledge
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, techniques or methods and ability to contribute to developing new ones Demonstrate knowledge of bioinformatics resources such as annotation tools and databases Demonstrate proficiency in R/BioConductor and
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on July 29, 2025:https://forms.office.com/r/695ZQxJt9G --- Applicants Must: Demonstrate: academic excellence and leadership in the research domain, high potential for success in their chosen fields, strong
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journals. Have experience in: (i) integrating single-cell and spatial multi-omics; (ii) computational programming in R, Python (and other common computer languages); (iii) competence/interest in analysing
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and commercial tools (e.g. XCMS, MZmine, Compound Discoverer, GNPS, SIRIUS, etc). Proficiency in one or more programming languages (e.g. R, Python). Experience with continuous integration and best
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large databases is required. The candidate is expected to partake in design of the epidemiological studies, statistical work (SPSS but also other programs such as R), presentations and preparation
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coding in R, Matlab and/or Python, and already adapt to using the UNIX command line and executing analyses using high performance computing cluster. Preferred qualifications: Prior experience in analyzing
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of Aqueous Sub-attoliter Compartments, J. Am. Chem. Soc. 2023, 145, 36, 19633–19641. - Malle, M. G., Löffler, P. M. G., Bohr, S. S.-R., Sletfjerding, M. B., Risgaard, N. A., Jensen, S. B., Zhang, M., Hedegaard