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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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, 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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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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, 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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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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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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-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
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technologies, is highly desirable. Skills: Strong knowledge of clinical informatics frameworks, standards, and methodologies. Proficiency in data analysis software (e.g., R, Python, SAS, SPSS, SQL) and
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analyses using statistical software, such as SAS, R, and others. Participate in group meetings and work collaboratively on larger projects with other group members. Generate data for figures and tables