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Field
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. · Familiarity with data analysis pipelines within R or similar computational language. · Strong writing and oral communication skills. · Excellent analytical thinking and creative troubleshooting skills
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modeling of mosquito-borne diseases as proven by peer reviewed publications. Demonstrably strong programming skills in R or Python. Experience with population biology, population genetics, or ecological and
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prostate tumor samples. This position requires a strong background in both experimental proteomics and computational data science (R and Python), with an emphasis on LC-MS/MS workflows and long-term cohort
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to Kiran R. Patil (kp533@mrc-tox.cam.ac.uk ) If you have any queries regarding the application process, please contact hradmin@mrc-tox.cam.ac.uk Further information can be found on our website: https
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, population genetics, or ecological and epidemiological modeling, and 3) an interest in mosquitoes and/or mosquito-borne diseases. The candidate should have demonstrably strong programming skills in R or Python
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data analysis. Record of scholarly publications and scientific communication skills Proficiency in statistical and data analysis tools (e.g., MATLAB, Python, R). Preferred Qualifications: Experience with
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. Responsibilities include cleaning datasets from recently completed research studies, database management, conducting statistical analyses (SAS or R), review/verification and archival of programs for research
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of Surrey, University of Leeds, UKCEH) and Chile (Universidad de Desarrollo and MICROB-R). You will use a system modelling approach to a) quantify available data, b) knowledge gaps and associated risks to c
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Biology, Bioinformatics, Computer Science, or a related field • Strong programming skills in R and/or Python • Experience with analysis of single-cell sequencing data • Familiarity with spatial
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-source tools and has a long history in coupling natural language techniques and knowledge graphs. The R&D programme for that position includes several tasks: Generalize and abstract the bot from specific