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manual data coding or crawling from online platforms; and (c) assist in data analysis with Stata and Python, among other activities. Qualifications Applicants should: (a) have an honours degree
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analysis, basic scripting in R or Python, and integration of multi-omic datasets is considered an advantage. Candidates should also possess a strong publication record in high-impact journals and a
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a strong background in one or more of the following areas: nonlinear (Lyapunov-based) analysis tools, software skills with Matlab or Python, and strong writing skills. Special Instructions
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have an interest in the use of AI in healthcare. Proven competence in programming methods in Python and similar environments is also essential. Informal enquiries may be addressed to Professor David
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methods in Python and similar environments is also essential. Informal enquiries may be addressed to Professor David Clifton (email: david.clifton@eng.ox.ac.uk). Only online applications received before
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, multi-task and pay attention to detail. Relevant research experience, knowledge of statistics, and familiarity with programming languages (e.g. R/Python/Matlab) are highly desirable. Prior experience
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and Python. Knowledge, Skills and Abilities: Solid understanding of common cellular and molecular mechanisms, early vertebrate development, skeletal physiology and genetics, associated structural birth
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. Demonstrated fluency in R, Python, or Stata programming languages and documentation (through prior research) of strong quantitative skills, as evidenced by application material. Ability to work both
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qualitative and quantitative data analysis. Proficiency in programming (e.g., JavaScript, Python, React for iOS/Android) is important. Additional strengths would include experience with physiological sensing
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., NeurIPS, ICML, ACL, EMNLP, etc.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding of transformer