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

Maastricht University; Maastricht; Hybrid; EUR 36,000 - 45,000; Full time

Benefits offered by this job

Home office allowance

Flexible working hours

Job summary

Maastricht University, in collaboration with Sciensano, invites applications for a PhD candidate in Network Modelling for Food Safety and Regulatory Decision-Making. The project develops new multi-layer network models for structured food-safety data, linking methodological research with policy-facing questions and real-world regulatory impact.

The candidate will work under a supervisory team from Maastricht University and Sciensano, focusing on methodological rigor, reproducible research, and

Qualifications

• Strong quantitative and computational background.
• Completed or near-completed master’s degree in a quantitative field.
• Experience with programming for data analysis or modelling in Python, R, or Julia.

Responsibilities

• Develop new network modelling approaches for structured food-safety data.
• Specify, estimate, validate, and interpret network models for regulatory decision-making.
• Analyze heterogeneous data relevant to food safety, contaminants, exposure, and regulation.
• Investigate how network models support contaminant risk assessment and monitoring prioritisation.
• Implement computational methods in reproducible research workflows.
• Publish results in international peer-reviewed journals and present at conferences.
• Collaborate with Maastricht University and Sciensano researchers and partners.
• Complete a doctoral dissertation within the agreed timeframe.

Skills

Quantitative background

Programming for data analysis

Python

R

Mathematical modelling

CEFR English (C1)

Education

Master's degree in a relevant field

Tools

Python

R

Julia

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 environment. The position offers:

• a 18-month contract, 1.0 FTE, with the prospect of a 2.5 year extension based on mutual satisfaction;
• a gross monthly salary between €3,204 and €4,051, based on full-time employment of 38 hours per week;
• 8% holiday allowance and an 8.3% year-end bonus;
• 29 vacation days, based on full-time employment, four additional days off, and the possibility to accrue up to 12 extra days through compensation hours;
• flexible working hours, a home office allowance, and the option to work from home;
• supervision by researchers from Maastricht University and Sciensano;
• the opportunity to develop new methodology in network modelling, computational analysis, and structured food-safety data;
• close collaboration with Sciensano, a public-health institute with established expertise in food safety, exposure assessment, contaminant risk analysis, and scientific evidence generation for public authorities;
• access to doctoral training, research seminars, methodological expertise, and professional-development opportunities;
• opportunities to publish in international journals and present at academic conferences;
• an interdisciplinary research environment combining methodological rigour with public-health and regulatory relevance;
• a solid pension plan via ABP, company fitness schemes, and access to various university sports facilities;
• an inspiring work environment in the heart of Europe.

As a PhD candidate, you will be automatically enrolled in one of our Graduate Schools. Find out more about our Graduate Schools.

Further information

For questions about the scientific content of the project, the research environment, or the collaboration between Maastricht University and Sciensano, please contact:

For questions about the application procedure or employment conditions, please contact:

Application procedure

Applications should include:

• a motivation letter describing the candidate’s interest in the position and fit with the project;
• transcripts or a list of completed courses and grades;
• a writing sample, such as a master’s thesis, research paper, or technical report;
• if available, examples of programming, modelling, or data-analysis work;
• the names and contact details of at least two academic referees.

In the motivation letter, candidates are encouraged to explain how their methodological background could contribute to the development of multi-layer network models for food safety and regulatory decision-making. Prior experience in food safety or public health is not required, but candidates should be willing to engage with domain experts and policy-facing research questions.

Applications should be submitted no later than 18 November 2026.

Please note that incomplete applications will not be considered. The vacancy is open for internal and external candidates. In case of equal qualifications, internal candidates will be prioritised.

At Maastricht University, we prefer to contact potential candidates directly. We therefore kindly ask that no agencies or intermediaries submit offers or approaches.

About the School of Business and Economics (SBE)

SBE is an international faculty for economics, business, and policy. We combine academic depth with societal engagement. Our education is small-scale and interactive. Our research focuses on themes such as sustainability, inclusion, and digital transformation. Together, we work on impactful solutions for the world of tomorrow.

About Maastricht University

At Maastricht University, we collaboratively seek solutions to help move the world forward. We do this with 23,300 students and 5,400 employees across 5 regional locations, 6 faculties, and more than 70 research institutes. We encourage you to push boundaries and discover new opportunities for yourself and the world around you. Together, we can find the answers for tomorrow.

The vacancy is open for internal and external candidates. In case of equal qualifications, internal candidates will be prioritised.

At Maastricht University, we prefer to contact potential candidates directly. We therefore kindly ask that no agencies or intermediaries submit offers or approaches.

Maastricht University is committed to promoting and nurturing a diverse and inclusive community. We believe that diversity in our staff and student population contributes to the quality of research and education at UM, and strive to enable this through inclusive policies and innovative projects led by teams of staff and students. We encourage you to apply for this position.