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(such as Python, R, Bash) Documented research experience in at least one of these: large-scale omics data analysis, machine learning and/or deep learning. Candidates with coursework or other relevant
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programming (such as Python, R, Bash) The candidate is required to have documented research experience with large-scale omics data analysis Experience with high performance computing is a strong advantage
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from buildings, mobile network data) Database management skills (e.g., PostgreSQL) Statistical expertise related to big data processing and high-performance computing (Python, R) GIS software proficiency
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from buildings, mobile network data) Database management skills (e.g., PostgreSQL) Statistical expertise related to big data processing and high-performance computing (Python, R) GIS software proficiency
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should hold a PhD degree in molecular biology, biochemistry, cancer biology or related fields. Expertise in biochemistry, transcriptomics, NGS data analysis and basic programming in R/Python is a pre
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. The student will focus on developing innovative computational methods, tools, and APIs in R (and Python) to support projects in toxicology, drug discovery, and sustainable material design. While prior biology
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R, algorithms and systems designed to extract insights from large datasets. Experience with integrating and analyzing diverse data sources, such as biodiversity (e.g., the IUCN Red List of Species and
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and machine learning tools, primarily in Python, Web/Cloud Services and NoSQL/SQL database (MongoDB/Postgres). Additional experience in R, JavaScript, ReactJS, Mechanical Turk would be advantageous but
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intake data and fundamental statistical knowledge. Proficiency with statistical software (particularly in R are considered an advantage). Excellent written and spoken English The candidate must enroll as a
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. Expertise in the use of relevant statistical software (e.g. STATA/R) is considered an asset. The position also requires strong English skills. Additionally, due to the scope of the research project knowledge