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cooperatively with others. Preferred Qualifications: • Proficiency in handling remotely sensed geospatial datasets and programming languages, particularly R or Python. • Knowledge, experience, and
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data. Statistical analysis using R and/or Python. Reproducible computational workflows. Scientific writing and publication. Microbiome research and host-associated microbial communities. The ideal
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including statistical programming (e.g., STATA, RStudio, Python). You will demonstrate previous experience of contributing to publications/presentations as well as the ability to manage your own academic
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, natural language processing, medical text mining, or clinical AI. Strong programming skills in Python and/or R. Familiarity with medical terminology, ICD codes, phenotype extraction, or HPO. Ability to work
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, Python, GitHub, fieldwork, laboratory workflows, or grant/manuscript development. Experience with R, Git/GitHub, phylogenomic or population genomic workflows, GIS/spatial analysis, herbarium curation
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with healthcare data (e.g., EHR, clinical text, imaging, omics). Proficiency in Python and ML tooling (e.g., PyTorch, scikit-learn), version control (Git), and experiment tracking (e.g., Weights & Biases
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models, and/or terrestrial biosphere models. 4. Proficiency in at least one programming language, such as MATLAB, R, or Python. 5. Ability to work effectively in an interdisciplinary and collaborative
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programming skills, with experience in R, Python or comparable scientific computing environments. · A record of rigorous research and peer-reviewed publication appropriate to career stage. · Ability to work
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, preprints, or thesis work. ● Proficiency in a scientific computing language (Python, MATLAB, Mathematica, Julia, R or C/C++). Preferred Qualifications: ● Prior experience with the Chemical Master
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proficiency in finite-element implementation through at least one of FEniCS/FEniCSx or Abaqus user subroutines (UEL/UMAT) in Fortran, together with scientific programming in Python. Knowledge, Skills