Research Assistant/Associate (AI Specialist)

About the Program

The MSc in Precision Health and Medicine x Artificial Intelligence (MScPHMxAI) programme at the National University of Singapore is a forward-looking graduate programme designed to train the next generation of healthcare, biomedical, and data-driven professionals in precision health and medicine.

The programme integrates emerging approaches in multi-omics, biomedical data science, artificial intelligence, machine learning, computational biology, digital health, and translational medicine. A key objective of the programme is to develop innovative, research-informed approaches to teaching, learning, student support, capstone training, industry engagement, and AI-enabled programme delivery.

We are seeking a motivated and capable Research Assistant(s)(at least 1) to support research and innovation activities within the MScPHM programme, with a particular focus on AI-enabled education, precision health learning support, programme analytics, computational infrastructure, and capstone research coordination.

Position Summary

The Research Assistant(s) will work closely with the Programme Directors, faculty members, students, and relevant academic and industry partners to support applied research and innovation in the MSc in Precision Health and Medicine programme.

The role will focus on developing, implementing, and evaluating AI-enabled approaches to enhance learning, research training, student support, programme operations, and capstone project development. The successful candidate will contribute to research on the effective and ethical use of AI in graduate education, learning analytics, precision health training, student progression, outreach strategy, industry alignment, and computational learning support.

This is a research-facing role with programme implementation responsibilities. The Research Assistant will support the generation of research outputs, reports, dashboards, workflows, educational resources, and evidence-based recommendations to improve the MScPHM programme and strengthen its role in precision health and medicine education.

Key Responsibilities

1. Applied Research in AI for Precision Health and Medicine Education

• Assist in research on the use of AI, machine learning, and data-driven methods to support learning in precision health and medicine.
• Support research into ethical, effective, and responsible approaches for using AI in graduate education, including how AI can enhance learning without compromising deep understanding, academic integrity, or independent thinking.
• Contribute to the design, implementation, and evaluation of AI-assisted learning workflows for MScPHM students.
• Assist in developing research frameworks to evaluate student learning needs, progression, engagement, and performance across the programme.
• Support the preparation of research reports, presentations, manuscripts, grant materials, and internal programme evaluation documents.
• Conduct literature reviews and benchmarking studies on AI in education, precision health training, biomedical data science education, and graduate programme innovation.

2. Capstone Research Support and Student Research Development

• Assist Programme Directors and faculty members in guiding students undertaking capstone projects involving AI, data science, multi-omics, bioinformatics, or precision health applications.
• Provide research and technical support to students working on computational or AI-related capstone projects.
• Help monitor capstone project progress and identify students who may require additional research or technical support.
• Support the development of capstone project resources, templates, research guidance materials, and computational learning aids.
• Assist in identifying suitable academic, clinical, research, and industry partners who may host or co-supervise capstone projects.
• Maintain and improve structured systems for tracking capstone topics, mentors, student progress, outputs, and project outcomes.
• Contribute to research on how capstone training can be better aligned with industry, healthcare, research, and workforce needs in precision health and medicine.

3. Programme Analytics, Student Progress Research, and Learning Support

• Assist in collecting, organising, analysing, and interpreting programme-related data, including student engagement, academic progression, assessment outcomes, capstone outcomes, and feedback.
• Develop dashboards, reports, and analytical summaries to help the programme team identify student needs and improve learning support.
• Support research-informed strategies to help students with varied computational backgrounds succeed in modules involving AI, data science, statistics, bioinformatics, or programming.
• Assist in organising research-informed computational support sessions, workshops, or clinics for students requiring additional help with programming, Linux, R, Python, AI/ML tools, or data analysis workflows.
• Support the development of learning materials for computational and AI-related components of the programme.
• Assist in evaluating the effectiveness of active learning tools, digital platforms, and AI-enabled learning interventions.
• Support academic integrity initiatives by helping evaluate tools and workflows related to assessment design, online learning, and responsible AI use.

4. AI-Enabled Workflow Development and Research Operations

• Identify opportunities to streamline research, learning support, student monitoring, capstone coordination, outreach tracking, and programme evaluation workflows.
• Design, implement, or recommend AI-assisted solutions, scripts, dashboards, forms, databases, and automation workflows to reduce repetitive manual work.
• Develop and maintain structured datasets related to student progression, capstone projects, mentor networks, industry engagement, outreach outcomes, and programme evaluation.
• Assist in building reproducible workflows for reporting, data cleaning, analysis, and visualisation.
• Support responsible use of AI tools in programme processes, including documentation, data privacy awareness, and quality control.
• Stay updated on emerging AI, automation, and educational technology tools relevant to graduate education and precision health training.

5. Computational and Technical Research Support

• Support the maintenance and management of programme-related computing environments, including Linux-based servers, user accounts, permissions, and relevant software environments.
• Assist students and faculty with basic troubleshooting of computational workflows used in teaching, learning, and capstone research.
• Support the use of high-performance computing, cluster, cloud, or server-based workflows where relevant.
• Help prepare technical documentation, guides, and reproducible examples for students using R, Python, Bash, Linux, bioinformatics tools, data analysis workflows, or AI/ML platforms.
• Assist in ensuring that computing environments are reliable, secure, and suitable for student learning and research activities.
• Work with relevant IT or institutional teams where needed to support programme-related technical requirements.

6. Research on Programme Positioning, Outreach, and Industry Alignment

• Assist in research to identify the profiles, motivations, and needs of prospective students who are best suited for the MScPHM programme.
• Support analysis of admissions trends, applicant backgrounds, recruitment channels, and outreach effectiveness.
• Help evaluate how the programme can better attract high-quality, best-fit applicants.
• Assist in identifying relevant research groups, clinical partners, companies, industry sectors, and organisations in precision health and medicine that may support capstone projects, internships, employment pathways, or collaborations.
• Maintain structured records of potential academic, clinical, and industry partners.
• Support the development of evidence-based outreach materials, digital content, programme summaries, and impact reports.
• Assist in analysing the impact of website updates, social media activities, information sessions, and other outreach initiatives.

7. Programme Implementation Support

• Supporting the preparation of learning materials, digital resources, workshops, and programme documentation.
• Assisting with coordination of research-related student activities, capstone briefings, workshops, seminars, and industry engagement sessions.
• Liaising with students, faculty, mentors, and partners on matters related to research activities, capstone coordination, technical support, and learning resources.
• Supporting the accurate organisation of programme-related data, records, reports, and documentation.
• Assisting with website and digital content updates related to programme research activities, capstone highlights, student achievements, and outreach initiatives.
• Assisting in organizing activities for the MScPHM students.

Qualifications

Essential Requirements

• A good Honours Bachelor’s degree or Master’s degree in computational biology, bioinformatics, data science, biomedical informatics, computer science, biomedical sciences, precision health, or a closely related field.
• Strong interest in AI, machine learning, data analytics, biomedical data science, or precision health and medicine.
• Strong programming skills in Python and/or R.
• Familiarity with Linux-based computing environments and basic Bash scripting.
• Ability to support students or research users with computational workflows, coding issues, data analysis, or reproducible research practices.
• Strong organisational skills and ability to manage multiple concurrent tasks, datasets, documents, and project timelines.
• Good written and verbal communication skills.
• Ability to work independently while collaborating effectively with faculty, students, administrators, and external partners.
• Strong attention to detail, especially in data management, documentation, reporting, and student progress tracking.

Desirable Requirements

• Experience with AI/ML methods, biomedical data analysis, bioinformatics pipelines, omics data, health data, or computational medicine.
• Experience supporting research projects, capstone projects, student projects, or academic programme evaluation.
• Experience with Linux server administration, user management, permissions, software installation, or computing infrastructure support.
• Experience with high-performance computing, cloud computing, clusters, or containerised environments.
• Familiarity with workflow automation, scripting, dashboards, data pipelines, or automated reporting.
• Experience with educational technology, learning analytics, digital learning platforms, or active learning tools.
• Experience with website content management, digital outreach, social media analytics, or science communication.
• Prior experience in a university, research, healthcare, biomedical, or education-related environment.
• Interest in scholarly work related to AI in education, precision health training, or biomedical data science education.