PhD Candidate in Network Modelling for Food Safety and Regulatory Decision-Making

About this position

Job description

Maastricht University’s School of Business and Economics, in collaboration with Sciensano, the Belgian institute for health and a leading public-health research organisation, is looking for a PhD candidate in Network Modelling for Food Safety and Regulatory Decision-Making.

Sciensano brings established expertise in food safety, food contaminants, exposure assessment, risk analysis, and scientific evidence generation for public authorities. The collaboration connects methodological research at Maastricht University with Sciensano’s policy-facing work supporting food safety assessment and risk-manager decision-making at Belgian and European levels.

The position focuses on the development of new multi-layer network modelling approaches for structured food-safety data. The aim is to support evidence generation for contaminant risk assessment, food safety monitoring, and regulatory decision-making.

The PhD candidate will conduct original methodological research while working in close connection with public-health and regulatory questions. The project offers a distinctive opportunity to develop rigorous computational models in a setting where scientific evidence, regulatory judgement, and societal impact are closely connected.

The project

This PhD project develops new multi-layer network modelling approaches for structured food-safety data. The project addresses an open methodological challenge: how to specify, estimate, validate, and interpret network models that can support evidence generation for contaminant risk assessment and regulatory decision-making.

The project focuses on the development of network-based models for analysing complex relationships in food safety data, including information relevant to contaminants, consumption patterns, exposure assessment, and regulatory prioritisation. The methodological contribution lies in developing models suited to this type of heterogeneous and relational data, rather than simply applying existing algorithms to a new domain.

The work builds on Sciensano’s established activities in food safety, exposure assessment, and contaminant risk analysis. Sciensano’s work supports science-based policy, legislation, and decision-making by risk managers at Belgian and European levels. This collaboration gives the project a strong societal and institutional grounding, while preserving a clear methodological research ambition.

The precise modelling choices and empirical focus will be developed as part of the PhD trajectory, in close dialogue with the supervisory team and Sciensano researchers. The candidate will be supervised by a team from Maastricht University’s School of Business and Economics and Sciensano, combining SBE’s methodological expertise with Sciensano’s expertise in food safety, while drawing on both institutions’ experience with regulatory decision-making.

The candidate will:

develop new network modelling approaches for structured food-safety data;

work on model specification, estimation, validation, interpretation, uncertainty assessment, and robustness analysis;

analyse heterogeneous data relevant to food safety, contaminants, consumption patterns, exposure assessment, and regulatory decision-making;

investigate how network models can support contaminant risk assessment, monitoring prioritisation, and evidence generation for food safety authorities;

implement computational methods in reproducible research workflows;

publish research in international peer-reviewed journals and present findings at academic conferences;

collaborate with researchers from Maastricht University, Sciensano, and relevant academic or policy-facing partners;

complete a doctoral dissertation within the agreed timeframe.

Candidate profile

We are looking for a candidate with a strong quantitative and computational background who is motivated to develop rigorous modelling approaches for societally relevant regulatory questions.

The candidate should have:

a completed or near-completed master’s degree in a relevant field, such as computer science, data science, applied mathematics, statistics, econometrics, operations research, physics, computational social science, systems science, or a related quantitative discipline;

demonstrated experience with programming for data analysis or computational modelling, preferably in Python, R, Julia, or a comparable language, and willingness to further develop skills in writing reproducible, modular, and maintainable research code;

a foundation in at least one relevant methodological area, such as network modelling, statistical learning, machine learning, probabilistic modelling, complex systems, or computational modelling, together with the ability and motivation to develop the specialised network-modelling expertise required for the project;

an interest in structured, relational, and heterogeneous data;

the ability to formulate research questions with both methodological and applied relevance;

strong analytical skills and the capacity to work independently;

strong written and spoken English communication skills, preferably at C1 level or higher according to the CEFR, with the ability to explain technical concepts to interdisciplinary academic, public-health, and policy-facing audiences;

ability and willingness to work in an interdisciplinary environment involving methodological, food-safety, public-health, and regulatory expertise.

Experience with food safety, contaminant risk assessment, exposure assessment, nutrition, toxicology, public health, or regulatory science is welcome but not required. Domain-specific knowledge can be developed during the project in collaboration with Sciensano. A strong technical profile and an interest in food safety decision-making are more important.

The starting date is negotiable, but ideally the position would start in January 2027.

Employment conditions

At Maastricht University, the PhD candidate will work in an international, open, and engaged academic