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Chris Thio

11 months ago

PhD Candidate in Exposomics, Machine Learning and Artificial Intelligence Utrecht University in Netherlands

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Expired

Country flag

Country

Netherlands

University

Utrecht University

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Keywords

Computer Science
Data Science
Epidemiology
Environmental Science
Biology
Artificial Intelligence
Environmental Health
Medical Science
Exposomics
Computational Modelling
High-dimensional Data
Statistics
Bioinformatics
Metabolomics
Genetics/genomics
Machinelearning
Quantitative Proteomics
microbial biology

About this position

Utrecht University is seeking a highly motivated PhD candidate to join the Institute for Risk Assessment Sciences (IRAS) within the Faculty of Veterinary Medicine. This position focuses on the integration of exposomics, machine learning, and artificial intelligence to advance precision prevention in human health. The successful candidate will design and apply innovative computational approaches—including graphical models, penalised structural models, and large language models—to analyze complex, high-dimensional, and time-varying exposure data. The research will integrate rich exposome datasets with multiple omics layers (genomics, proteomics, metabolomics, and microbiomics) to uncover how environmental factors influence human health at the molecular level.

The PhD candidate will work closely with leading experts in epidemiology, omics, and environmental health as part of the Gravitation program Exposome-NL, a national research consortium uniting over fifty scientists from diverse disciplines across the Netherlands. The position is based in the Data Science & Exposome Group within the One Health Chemical research line, led by Professor Roel Vermeulen, and will involve collaboration with several interconnected groups, including Occupational Health, Environmental Health, Planetary Health, and Exposure Science.

Applicants should hold a Master's degree in Data Science, Statistics, (Computational) Epidemiology, Bioinformatics, or a related field, and demonstrate expertise in programming and quantitative data analysis using R or Python. A strong interest in high-dimensional omics and exposome research, excellent collaboration, reporting, and presentation skills, and proficiency in English are required. Experience with machine learning or AI pipelines and familiarity with high-performance computing environments are highly desirable.

The position offers a one-year contract, extendable to four years upon positive evaluation, with a gross monthly salary between €3,059 and €3,881 (salary scale P under the Collective Labour Agreement for Dutch Universities). Additional benefits include 8% holiday pay, 8.3% year-end bonus, a pension scheme, partially paid parental leave, flexible terms of employment, and professional development opportunities. Utrecht University is committed to diversity and inclusion, providing a supportive environment for all staff and students.

To apply, submit your motivation letter, CV, and contact details of at least two referees via the university's online application portal before the deadline of 5 January 2026. For further information, prospective applicants may contact Dr. Chris Thio or Dr. Natalia Vilor-Tejedor.

Funding details

Available

What's required

Applicants must have a Master's degree in Data Science, Statistics, (Computational) Epidemiology, Bioinformatics, or a related field. Demonstrated expertise in programming and quantitative data analysis in R or Python is required. Candidates should have a strong interest in high-dimensional omics and exposome research, excellent collaboration, reporting, and presentation skills, and proficiency in written and spoken English. Experience with machine learning or AI pipelines and familiarity with high-performance computing environments are especially encouraged.

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Professors

Prof. at Utrecht University

University Name
Country flag

Netherlands

Research Interests

Epidemiology

Biostatistics

Environmental Science

Biology

Biomarker Research