NTNU NutriGUT
Research Field
Nguyen Thi Kim Ngan, MD, PhD, is an Assistant Professor in the Programs of Undergraduate and Graduate Nutrition Science at National Taiwan Normal University (NTNU), one of Taiwan’s top institutions for nutrition training. With a background as a medical doctor, she specializes in obesity and metabolic disorders and has developed expertise in pediatric precision nutrition. Her research encompasses lipid metabolism, biostatistics, systematic reviews, and machine learning, and digital health.
Through her recent publications and projects funded by Taiwan’s National Science and Technology Council (NSTC), she has significantly contributed to understanding various aspects of children's diseases, including early puberty, functional gastrointestinal diseases (FGIDs), and childhood obesity. Her work in lipidomic, metabolomics, and gut microbiota has led to the development of predictive models for disease diagnosis and management. In her pursuit of precision nutrition, she has conducted research on artificial intelligence to integrate omics data, aiming for early detection and targeted, individualized treatment by exploring the underlying mechanisms in greater depth.
At the Nutrition Science and Precision Medicine Lab, we are dedicated to advancing the understanding of nutrition and its impact on human health through cutting-edge research. Our mission is to bridge the gap between nutrition science and medical practice by focusing on personalized approaches to disease prevention and management.
Research Focus: Our lab is committed to improving health through innovative research in nutrition and personalized medicine. We focus on understanding how diet, gut health, and sleep affect children's development and overall well-being.
Key Areas of Research:
- Improved Pediatric Health: Our research helps identify the links between gut health, behavior, and sleep in children, leading to better management of these conditions and improving the lives of children and their families.
- Gut Microbiota and Gastrointestinal Health: Exploring the intricate relationship between gut microbiota and functional gastrointestinal diseases (FGIDs), our research aims to unravel how dietary interventions can modulate gut health and improve outcomes.
- Lipid Metabolism and Metabolomics: Investigating the role of lipids in health and disease, we use advanced lipidomic and metabolomic techniques to uncover biomarkers and mechanisms underlying metabolic disorders.
- Systematic Reviews and Evidence Synthesis: Our lab conducts comprehensive systematic reviews to synthesize the latest evidence in nutrition science, providing a solid foundation for future research and clinical guidelines.
- Machine Learning and Biostatistics: Leveraging machine learning and biostatistical methods, we create predictive models for disease diagnosis and personalized nutrition plans, enhancing the precision and effectiveness of dietary recommendations.
- Digital health/Interdisciplinary advancements: The study bridges multiple disciplines, including gastroenterology, neuroscience, microbiology, and AI, contributing valuable knowledge to each field.
- 2023.10, Best Oral Presenter Award (The 23rd Congress of The Parenteral and Enteral Nutrition Society of Asia – PENSA2023, Taiwan)
- 2019.09, Best Oral Presenter Award - Travel grant (The 2019 International Congress of Diabetes and Metabolism-ICDM2019, Seoul, South Korea)
- 2018.10., Most Popular Vote Award, Artificial intelligence “Drug – Smart Advisor” App (TMU x MIT Hackathon 2018, Taipei Medical University, Taiwan & Massachusetts Institute of Technology, USA)
- 2018.09, Best Oral Poster Presenter Award - Travel grant (The 40th European Society for Clinical Nutrition and Metabolism Congress-ESPEN2018, Madrid, Spain).
- 2025, Teachers College (EMI intensive training program), Columbia University, the USA.
- 2018 – 2022, PhD., School of Nutrition and Health Sciences, Taipei Medical University, Taiwan (Full-bright scholarship).
- 2016 – 2018, M.S., International Master Program in Medicine, Taipei Medical University, Taiwan (MOST Full-bright scholarship).
- 2015 – 2016, Diploma of Therapeutic Respiratory, University of Medicine and Pharmacy Ho Chi Minh-Vietnam, Corse University and Descartes University, France.
- 2008 – 2014, MD., University of Medicine and Pharmacy Ho Chi Minh city, Vietnam.
- 2006 – 2010, Bachelor of Economic, University of Economics and Law, Vietnam National University Ho Chi Minh city, Vietnam.
Job Description
- Bioinformatics & Omics Data Analysis
- Process and analyze biological datasets (e.g., gut microbiome, metabolomics, clinical metadata).
- Apply statistical and machine learning approaches to identify biomarkers relevant to pediatric health.
- Assist in data visualization and interpretation for research outputs.
Preferred Intern Educational Level
The position is strongly preferred to Final-year undergraduate or Master’s student who specialize in: Bioinformatics, Data Science, Biomedical Engineering, Computer Science (AI/ML focus), Health Informatics. It is also acceptable to early PhD student (Year 1–2).
Skill sets or Qualities
Candidate should have:
- Basic programming skills, with experience in Python required and some familiarity with R considered a plus, particularly for data analysis or bioinformatics.
- Experience with machine learning, and a basic understanding of image classification or convolutional neural networks (CNNs).
- Some exposure to bioinformatics or biological data analysis, such as microbiome (16S or shotgun sequencing), metabolomics, or other omics datasets
- Familiarity with app or system integration is a plus, including basic knowledge of APIs, databases, cloud platforms, or mobile/web frameworks (e.g., Flutter or React).
- Experience integrating machine learning models into applications is beneficial but not required.
Job Description
- Bioinformatics & Omics Data Analysis
- Process and analyze biological datasets (e.g., gut microbiome, metabolomics, clinical metadata).
- Apply statistical and machine learning approaches to identify biomarkers relevant to pediatric health.
- Assist in data visualization and interpretation for research outputs.
Preferred Intern Educational Level
The position is strongly preferred to Final-year undergraduate or Master’s student in Nutrition Science-Clinical Nutrition, Public Health / Epidemiology, Health Sciences, Biostatistics (with interest in nutrition). The job is also opened to early PhD student (Year 1–2) seeking structured training in evidence synthesis.
Skill sets or Qualities
Candidate should have:
- Basic understanding of clinical nutrition and pediatric health, along with familiarity in reading and interpreting scientific research articles.
- Introductory knowledge of systematic review and meta-analysis methodology (RCT, cohort studies, and case–control studies).
- Basic statistical knowledge and have experience with or a willingness to learn meta-analysis software such as RevMan, R, or Stata.
- Organize, manage, and curate research data using tools such as Excel or Google Sheets i
- Experience searching academic literature databases, preferably PubMed, and demonstrate an understanding of, or willingness to learn, PRISMA guidelines.
- Strong academic writing skills in English.