Research Fellow, Data Science & Digital Health Innovation [LKCMedicine]

The Lee Kong Chian School of Medicine (LKCMedicine) trains doctors who put patients at the centre of their exemplary care. The School, which offers both undergraduate and graduate programmes, is named after local philanthropist Tan Sri Dato Lee Kong Chian. Established in 2010 by Nanyang Technological University, Singapore, in partnership with Imperial College London, LKCMedicine aims to be a model for innovative medical education and a centre for transformative research. The School’s primary clinical partner is the National Healthcare Group, a leader in public healthcare recognised for the quality of its medical expertise, facilities and teaching. The School is transitioning to an NTU medical school ahead of the 2028 successful conclusion of the NTU-Imperial partnership to set up a Joint Medical School. In August 2024, we welcomed our first intake of the NTU MBBS programme, that has been recently enhanced to include themes like precision medicine and Artificial Intelligence (AI) in healthcare, with an expanded scope in the medical humanities. Graduates from the five-year undergraduate medical degree programme will have a strong understanding of the scientific basis of medicine, with an emphasis on technology, data science and the humanities.

Asst Prof Sabrina Wong is Clinical Director or the Primary Care & Family Medicine group from LKCMedicine. She is also a Senior Consultant Family Physician and a Principal Clinician Scientist. She leads the Precision Digital Health lab, which was established to transform chronic disease management through advanced technology. The lab focuses on AI-driven, person-centered care, and it aims to harness generative AI and digital phenotyping to predict, prevent and personalise health interventions.

We are looking for a Research Fellow with expertise in data science to advance research in digital health innovation. This role is integral to LKC’s mission of leveraging technology to transform healthcare and improve population health outcomes.

Key Responsibilities:

• Design and implement advanced AI/ML models for healthcare applications, including predictive analytics and generative AI solutions.

• Develop and validate digital phenotyping algorithms using multimodal data (e.g., wearable sensors, EHRs, behavioral data) to detect early signs of chronic conditions such as cardiometabolic, mental health, and frailty-related disorders.

• Build generative AI systems to support personalized lifestyle interventions and clinical decision-making for chronic disease management.

• Perform large-scale data integration and analysis, ensuring data quality, security, and compliance with ethical standards.

• Collaborate with clinicians and behavioral scientists to translate computational models into actionable healthcare solutions.

• Publish high-impact research, contribute to grant proposals, and present findings at international conferences.

• Mentor junior researchers and interns, fostering a culture of innovation and excellence in data-driven healthcare research.

Key Competencies and Requirements:

• PhD in Data Science, Computer Science, Biomedical Informatics or related field

• At least 3 years of relevant experience in data science or analytics role

• Strong analytical and problem-solving skills, with the ability to translate complex data into actionable insights.

• Proficiency in data analysis tools and programming languages (e.g., SQL, Python, R) for data manipulation, analysis, and visualization.

• Familiarity with statistical techniques and data mining methods.

• Knowledge of database concepts and experience working with large datasets (good to have).

• Strong attention to detail and ability to handle and analyze data accurately.

• Excellent communication and presentation skills to effectively convey analytical findings to both technical and non-technical stakeholders.

• Ability to work independently and collaboratively in a fast-paced environment.

We regret to inform that only shortlisted candidates will be notified.