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PhD in AI for Health | University of Oxford
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Researcher with diverse experience in the state of the art in deep learning and computer vision.
University of Oxford
Doctor of Philosophy - PhD
January 1, 2019 – January 1, 2023
Imperial College London
Master's degree, Bioengineering and Biomedical Engineering
January 1, 2013 – January 1, 2017
The World Bank
Consultant Researcher
May 1, 2025 – January 1, 2026
Remote
University of Oxford
PhD Candidate
September 1, 2019 – January 1, 2025
Samsung Electronics
Machine Learning Engineer
October 1, 2017 – September 1, 2019
Staines, Surrey, United Kingdom
Samsung Electronics
Machine Learning Intern
July 1, 2017 – September 1, 2017
Staines, Surrey, United Kingdom
BICV Group, Imperial College London
Masters Student
October 1, 2016 – June 1, 2017
Imperial College London
Undergraduate Research Assistant
June 1, 2015 – September 1, 2015
London, United Kingdom
Cultural Fit Analysis
The candidate's background is heavily weighted towards academic research and R&D roles, with significant time spent in PhD studies. While there is industry experience at Samsung, the overall profile suggests a strong preference for research-intensive environments. The project diversity is focused within deep learning and computer vision, which aligns with an ML Engineer role, but the breadth of application areas outside of medical imaging and satellite imagery is limited. The long academic tenure might require adjustment to typical industry development cycles and product-focused delivery.
Soft Skills & Operational Fit
The candidate demonstrates strong collaboration skills through interdisciplinary research and co-supervision. Experience in presenting complex projects and securing funding indicates strong communication and persuasive abilities. The independent work on proof-of-concept demos suggests initiative and problem-solving capabilities. However, specific operational fit for a fast-paced industry environment beyond research is not explicitly detailed.