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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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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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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
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related field. Extensive experience with R and Python, and prior experience in computational biology are required. The candidate must have excellent written and verbal communication skills, the ability
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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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plan and conduct data processing and statistical analyses. Knowledge of statistics and programming (e.g., R) is therefore a requirement. The candidate is expected to compile research results, interpret
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, single-cell analysis, and machine/deep learning (preferred but not required). Strong programming and statistical skills (e.g., Python, Perl, R, Bash). Track record of first-author research papers. Strong
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-intensive field Proficiency in Python (or R), version control, and clean code practices Experience with omics data analysis and integration Hands-on expertise in developing and fitting executable models