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Maastricht University
Closing soon
3 days ago
PhD Candidate in Network Modelling for Food Safety and Regulatory Decision-Making Maastricht University in Netherlands
Degree Level
PhD
Field of study
Computer Science
Funding
Gross monthly salary €3,204–€4,051 for 38 hours/week, plus 8% holiday allowance and an 8.3% year-end bonus; 18-month contract with prospect of a 2.5-year extension.
Deadline
Nov 18, 2026
Country
Netherlands
University
Maastricht University
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About this position
Maastricht University’s School of Business and Economics, in collaboration with Sciensano, is offering a PhD project on multi-layer network modelling for structured food-safety data. The research sits at the intersection of methodological modelling and regulatory decision-making, with a clear focus on evidence generation for contaminant risk assessment, food safety monitoring, and prioritisation for public authorities.
The project is designed to develop new network-based approaches for analysing heterogeneous and relational food-safety data, including information on contaminants, consumption patterns, exposure assessment, and related regulatory questions. Rather than applying existing algorithms as-is, the PhD candidate will contribute to the development of modelling methods that are specifically suited to the structure and complexity of food-safety evidence.
The position is embedded in a collaboration between Maastricht University and Sciensano, the Belgian institute for health, which brings strong expertise in food safety, food contaminants, exposure assessment, risk analysis, and scientific evidence generation for public authorities. The candidate will work in close connection with researchers from both institutions and will address problems with direct relevance to science-based policy, legislation, and risk-management support at Belgian and European levels.
The research environment combines Maastricht University’s methodological expertise with Sciensano’s applied public-health and food-safety perspective. The successful candidate will work on model specification, estimation, validation, interpretation, uncertainty assessment, and robustness analysis, and will implement computational methods in reproducible research workflows. The project also offers opportunities to publish in peer-reviewed journals, present at academic conferences, and complete a doctoral dissertation within the agreed timeframe.
The PhD is located within Maastricht University’s School of Business and Economics and is part of an interdisciplinary setting that connects quantitative methods with public-health and regulatory relevance.
Funding details
Gross monthly salary €3,204–€4,051 for 38 hours/week, plus 8% holiday allowance and an 8.3% year-end bonus; 18-month contract with prospect of a 2.5-year extension.
What's required
A completed or near-completed master’s degree in a relevant quantitative field such as computer science, data science, applied mathematics, statistics, econometrics, operations research, physics, computational social science, systems science, or a related discipline. Demonstrated programming experience for data analysis or computational modelling, preferably in Python, R, Julia, or a comparable language, and willingness to develop reproducible, modular, maintainable research code. A foundation in at least one methodological area such as network modelling, statistical learning, machine learning, probabilistic modelling, complex systems, or computational modelling. Interest in structured, relational, and heterogeneous data; ability to formulate research questions with methodological and applied relevance; strong analytical skills and ability to work independently. Strong written and spoken English, preferably at C1 level or higher according to CEFR, and ability to explain technical concepts to interdisciplinary audiences. Ability and willingness to work in an interdisciplinary environment. Experience with food safety, contaminant risk assessment, exposure assessment, nutrition, toxicology, public health, or regulatory science is welcome but not required.
How to apply
Submit an application via the AcademicTransfer application link. Include a motivation letter, CV, transcripts or course list with grades, a writing sample, examples of programming/modelling work if available, and contact details of at least two academic referees. Submit no later than 18 November 2026; incomplete applications will not be considered.
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