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

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

• 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.
• 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.
• 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.
• Rui Jorge Almeida via rj.almeida@maastrichtuniversity.nl
• Mirjana Andjelkovic via Mirjana.Andjelkovic@sciensano.be
• Miranda Van den Beuken via miranda.vandenbeuken@maastrichtuniversity.nl
• a motivation letter describing the candidate’s interest in the position and fit with the project;
• a curriculum vitae;
• 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.