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Field
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research, epidemiology, biostatistics, data science, economics, pharmacy, medicine, or related fields Additional Qualifications Strong quantitative and programming skills, including proficiency in R, Python
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catalysis. Strong programming skills in Python; experience with scientific computing and data analysis. Experience with first-principles calculations (e.g., DFT), molecular dynamics, or machine learning
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(or related field) Advanced mastery of statistical programming in R/Python (or equivalent) Strong data analysis/data management skills Excellent written communication skills and experience with manuscript/grant
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recognition; (b) experience in system development and integration; and (c) proficiency in using Python or other scripting languages. Applicants are invited to contact Prof. Kong Aik Lee at telephone
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and library preparation is required. The applicant must have strong independent bioinformatics and programming skills in R, Python or equivalent. The applicant must be able to perform end-to-end
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programming, such as R and/or Python is required. Able to work independently, meet specific goals and milestones, but also serve as part of a collaborative interdisciplinary team. Additional Qualifications
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) statistics and data science Experience with mathematical modelling (e.g., linear algebra, linear models methodologies) Demonstrated programming skills in C/C++, Python, R or comparable languages. Experience
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observations and field data collection. Strong competence in data preparation/analysis/visualization with Python, R, or a similar scripting and visualization language. Experience with high performance computing
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scientific discipline or equivalent experience is required. Additional Qualifications ● Should have a demonstrated proficiency in R and/or Python, or related languages. ● Experience with and solid understanding
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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