Product Development Automation Lead

The Product Development Automation Lead plays a key role in renewing the research and production strategy of Wageningen Food Views (WFV). This role focuses on developing and implementing AI solutions that accelerate and automate the calculation of scores within various Food Views products. The position operates within DSI’s central metadata‑driven data architecture, where data quality, governance, version control and traceability are essential principles.

The Automation Lead combines AI expertise with a strong understanding of data management and workflow‑driven processing, developing sustainable automation solutions that can be deployed across the WSER‑wide data infrastructure.

The Product Development Automation Lead works closely with data scientists, domain experts and product teams to establish a new standard for data‑driven insights in the food and sustainability sector, fundamentally transforming the WFV business model.

Your key tasks and responsibilities:

• Develops and implements AI‑driven automation solutions
  Builds and integrates AI components (e.g. classification, extraction, validation, semantic enrichment) within DSI’s metadata‑driven architecture to accelerate and standardise calculations, scoring models and data processing.
• Designs and maintains scalable dimensional data models
  Responsible for designing, maintaining and applying dimensional data models that translate research and production data into consistent, traceable and reusable datasets, improving the data quality of WFV products.
• Standardises and optimises research and production processes
  Develops sustainable automation patterns reusable across multiple research and product domains, structurally reducing research costs while increasing update frequency and product coverage.
• Works closely with data scientists, domain experts and product teams
  Aligns with researchers, business development and the Data & AI Expertise Team; translates substantive needs into technical solutions and contributes to an integrated WSER‑wide data infrastructure.
• Contributes actively to the strategic growth of Wageningen Food Views
  Deploys AI technology to replace repetitive work, improves the quality and reliability of indicators and scores, and directly contributes to the scalability, innovation capacity and market impact of WFV.

Your Team
The new colleague will work within the DSI team of WSER and participate in the core team of Wageningen Food Views. You will collaborate with researchers, domain experts, business development, and the data management team (AI expertise, data scientists, and product teams). The role is an integral part of WSER’s central data and AI infrastructure.

Your Workplace
Wageningen Social & Economic Research (WSER) employs around 390 staff across locations in The Hague, Wageningen and Lelystad, as well as several smaller regional offices. They work within 11 research teams on multidisciplinary projects tackling complex socio‑economic challenges, such as climate, healthy and safe food, sustainable living environments and sustainable incomes in the Netherlands and beyond.
One of these 11 teams is Data Science and Information Management (DSI).

DSI (approx. 30 colleagues) specialises in Data Science, Business Informatics, Digital Governance, Digital Innovation Ecosystems, Data‑Driven Business Models, and the Ethical, Legal & Social Aspects of AI. DSI collaborates with all other research groups on market‑oriented research within WSER’s broad domain. Together with other WSER groups, DSI works nationally and internationally for governments, NGOs and the business community.

Product Development Automation Lead

De Product Development Automation Lead speelt een sleutelrol in het vernieuwen van de onderzoeks- en productiestrategie van Wageningen Food Views (WFV). Deze functie richt zich op het ontwikkelen en implementeren van AI-oplossingen die de berekening van scores binnen de verschillende Food Views-producten versnellen en automatiseren. De rol opereert binnen de centrale metadata-gedreven data-architectuur van DSI, waar datakwaliteit, governance, versiebeheer en herleidbaarheid belangrijke uitgangspunten zijn.

De Automation Lead combineert AI-expertise met een goed begrip van datamanagement en workflow-gedreven verwerking, en ontwikkelt automatiseringsoplossingen die duurzaam inzetbaar zijn in de WSER-breed gebruikte data-infrastructuur.

De Product Development Automation Lead werkt nauw samen met data scientists, domeinexperts en productteams om een nieuwe standaard te zetten voor data-gedreven inzichten in de voedsel- en duurzaamheidssector en zo het businessmodel van WFV fundamenteel te transformeren.

Jouw taken en verantwoordelijkheden als Product Development Automation Lead zijn:

• Ontwikkelt en implementeert AI‑gedreven automatiseringsoplossingen
  Bouwt en integreert AI‑componenten (o.a. classificatie, extractie, validatie, semantische verrijking) binnen de metadata‑gedreven architectuur van DSI om berekeningen, scoremodellen en dataverwerking te versnellen en te standaardiseren.
• Ontwerpt en onderhoudt schaalbare dimensionele datamodellen
  Verantwoordelijk voor het ontwerpen, onderhouden en toepassen van dimensionele datamodellen die onderzoeks‑ en productiedata vertalen naar consistente, herleidbare en herbruikbare datasets en zo de datakwaliteit van WFV‑producten verhogen.
• Standaardiseert en optimaliseert onderzoeks- en productieprocessen
  Ontwikkelt duurzame automatiseringspatronen die herbruikbaar zijn binnen meerdere onderzoeks- en productdomeinen, verlaagt hiermee structureel de onderzoekskosten en verhoogt updatefrequentie en productdekking.
• Werkt nauw samen met data scientists, domeinexperts en productteams
  Zorgt voor afstemming met onderzoekers, business development en het data‑ & AI‑expertiseteam; vertaalt inhoudelijke behoeften naar technische oplossingen en draagt bij aan een geïntegreerde WSER‑brede data‑infrastructuur.
• Draagt actief bij aan de strategische groei van Wageningen Food Views
  Zet AI‑technologie in om repeterend werk te vervangen, verbetert de kwaliteit en betrouwbaarheid van indicatoren en scores en levert zo een directe bijdrage aan de schaalbaarheid, innovatiekracht en marktimpact van WFV.

Jouw team
De nieuwe collega zal vanuit zijn plaats in het DSI team van WSER deel nemen in het Wageningen Food Views kernteam en zal samenwerken met onderzoekers, domeinexperts, business development, en data management team (AI expertise, data scientists en productteams). De rol maakt volwaardig onderdeel uit van de centrale data- en AI-infrastructuur van WSER.

Hier ga je werken
Wageningen Social & Economic Research (WSER) employs approximately 390 people across locations in The Hague, Wageningen, and Lelystad, as well as a number of smaller offices in the regions. They work in 11 research teams on multidisciplinary research projects, collaborating on challenging socio-economic issues. This means that you will be working on solutions to today's major challenges: climate, healthy and safe food for everyone, a sustainable living environment, and sustainable income in the Netherlands and worldwide. One of those 11 teams is Data Science and Information Management (DSI).

DSI (approx. 30 colleagues) is the expert in the field of Data Science, Business Informatics, Digital Governance, Digital Innovation Ecosystems, Data-driven Business Models, and the Ethical, Legal & Social Aspects of AI. DSI collaborates with all other research groups on market-oriented research in the broad domain of WSER. Together with the other WSER groups, DSI operates in a national and international market of governments, NGOs, and the business community, providing research products and advice.

Postdoctoral Position (1 FTE): Emotions in Regulatory Politics

This two-year postdoctoral position is part of the VIDI research project “Emotion-Infused Risk Regulation: Rethinking the Fundamentals of Regulatory Governance for Citizen-Centric Rulemaking”, funded by the Dutch Research Council (NWO) and led by Dr. Dovilė Rimkutė.

The VIDI project investigates the role of emotions in regulatory governance, examining how emotions shape both regulators’ formulation of rules and citizens’ reception of regulatory responses. It is structured around three interlinked objectives: (1) Emotion and Regulatory Responsiveness: To what extent and why do regulators respond to citizens’ emotion-laden concerns? (2) Citizens’ Emotional Responses in Regulatory Encounters: To what extent and how are citizens’ emotions elicited during interactions with regulators? (3) Emotions and Regulatory Legitimacy: What are the effects of citizens’ emotions on their perceptions of regulators’ legitimacy? Read more about this research agenda in the following link: Affective regulatory governance: towards an emotion-based understanding of citizen-regulator interactions in regulatory politics.

The postdoctoral project will contribute to the emerging research agenda on affective regulatory governance, with a particular focus on citizens’ emotions in the Regulatory State context (Objectives 2 and 3). Specifically, the project will examine emotionality in citizens’ interactions with the regulatory state and experimentally test causal effects and mechanisms through which emotions influence citizens’ perceptions of regulators’ legitimacy.

Your duties
As a postdoctoral researcher, you will contribute to the implementation of three empirical studies planned within Objectives 1 and 2 of the Vidi project. In addition, you will have the opportunity to develop and pursue your own research ideas within the broader topic of the project (e.g., Emotion in Public Governance).

You will be responsible for designing and implementing experimental studies (primarily large-scale survey experiments) and applying (advanced) quantitative analysis methods. Experience with laboratory-based experiments can also be used or developed within the project.

Working closely with an interdisciplinary research team within the Regulatory Behavior Lab, led by Dr. Dovilė Rimkutė, you will play a central role in advancing the project’s scientific contributions. Your responsibilities will include refining cross-national survey experiments, administering data collection with representative citizen samples in at least six EU countries and beyond, analysing quantitative data, and disseminating findings through conference presentations, publications in high-quality international journals, and publicly accessible policy outputs (e.g., reports and policy briefs).

As a core member of the recently founded Regulatory Behavior Lab, you will actively contribute to shaping its scientific profile and visibility. This includes participating in lab meetings, engaging in collaborative research, and helping build connections between academic and practitioner communities (for example, by co-organizing workshops and events that translate research findings into actionable insights for regulatory practice).

The allocation of your time will be determined in consultation with the Principal Investigator, with a typical balance of approximately 0.8 FTE dedicated to research and 0.2 FTE to teaching activities, such as supervising MA theses within the department’s teaching programmes.

If desired, you may also act as a formal co-supervisor for the PhD candidate working on Objective 1 of the Vidi project, gaining experience in PhD supervision and contributing to the candidate’s academic development.

These tasks are intended as development opportunities and will be performed under guidance, depending on your experience and growth within the role.

PhD Position (1 FTE): Emotions in Regulatory Politics

This fully funded four-year PhD position is part of the VIDI research project “Emotion-Infused Risk Regulation: Rethinking the Fundamentals of Regulatory Governance for Citizen-Centric Rulemaking”, funded by the Dutch Research Council (NWO) and led by Dr. Dovilė Rimkutė.

The VIDI project investigates the role of emotions in regulatory governance, examining how emotions shape both regulators’ formulation of rules and citizens’ reception of regulatory responses. It is structured around three interlinked objectives: (1) Emotion and Regulatory Responsiveness: To what extent and why do regulators respond to citizens’ emotion-laden concerns? (2) Citizens’ Emotional Responses in Regulatory Encounters: To what extent and how are citizens’ emotions elicited during interactions with regulators? (3) Emotions and Regulatory Legitimacy: What are the effects of citizens’ emotions on their perceptions of regulators’ legitimacy? Read more about this research agenda in the following link: Affective regulatory governance: towards an emotion-based understanding of citizen-regulator interactions in regulatory politics.

The PhD project will contribute to the emerging research agenda on affective regulatory governance, with a particular focus on regulatory responsiveness to citizens’ emotionally charged concerns (Objective 1). Specifically, the project will examine when and under what conditions regulators respond to citizens’ emotion-laden reactions - such as anger, fear, or anxiety regarding regulatory conduct. The project will analyze these dynamics across different regulatory domains (e.g., health, environment, financial regulation) and multiple levels of the EU governance system (supranational and national). It will investigate how independent regulators exercise their discretionary authority when responding to affective citizen concerns, particularly when addressing emerging societal risks that demand regulatory interventions. Empirically, the PhD project employs a rigorous mixed-methods design, combining survey experiments with interviews involving top-level EU and national regulators.

Your duties
As a PhD candidate, you will complete a cumulative dissertation within four years at the Department of Political Science and Public Administration. Your dissertation will consist of approximately six chapters, including four experimental and qualitative studies prepared as journal articles.

Working closely with your interdisciplinary supervisory team led by Dr. Dovilė Rimkutė, you will play an integral role in advancing the VIDI project’s first objective on regulatory responsiveness. You will design and conduct survey experiments and semi-structured interviews with EU and national regulators, collect and analyze both quantitative and qualitative data, and disseminating findings through conference presentations, publications in high-quality international journals, and publicly accessible policy outputs (e.g., reports and policy briefs).

The allocation of your time will be determined in consultation with the Principal Investigator, with a typical balance of approximately 0.95 FTE dedicated to research and 0.05 FTE to teaching activities within the department’s teaching programmes.

As a core member of the recently founded Regulatory Behavior Lab, you will actively contribute to shaping its scientific profile and visibility. You will engage in collaborative research, participate in lab meetings, and help bridge academic and practitioner communities—for instance, by co-organizing workshops and events that translate the project’s findings into actionable insights for regulatory practice.

As a member of our vibrant, internationally renowned department, you will broaden your professional network and develop collaborations with scholars in related fields. All PhD candidates of the Faculty of Social Sciences will be enrolled in the Graduate School of Social Sciences (VU-GSSS) and admission to the VU-GSSS is part of the hiring procedure. More details can be found on their website and are available upon request.

PhD in Optimal Operation of Multicommodity Energy Systems from a Market and Grid Perspective

The BACH (Brainport Approach for a Congestion-free Holland) project addresses the increasing congestion in Dutch electricity distribution grids caused by the rapid growth of renewable energy generation and increased electrification of energy demand. Due to limited grid capacity and long connection queues, customers can no longer get a new connection, or expansion of an existing connection, this holds for both demand and supply. A large share of renewable generation cannot be connected or must be curtailed.

BACH develops a data-driven and proactive approach to congestion mitigation by integrating electricity, heat and gas systems into a multicommodity energy system. The optimal operation of a multicommodity energy system is demonstrated on TU/e campus. The real-life project data and results will be enriched to make them applicable on a large scale. Using distribution grid data, customer profiles and energy transition scenario’s, the project identifies where storage and conversion technologies can be deployed most effectively in the future to relieve grid congestion and increase renewable energy integration.

A central element of BACH is the development of an open-architecture Multicommodity Energy Management System (MC-EMS) and associated impact analysis tools, which together enable grid-aware and market-aware operation of multicommodity energy hubs. The concepts and tools are validated in a real-life mirror location at the TU/e campus and designed for scalability towards regional and national application.

This PhD project focuses on the modeling and optimal operation of multicommodity energy systems under explicit distribution grid constraints. The research addresses how real grid congestion information can be systematically incorporated into system models and operational decision-making.

The PhD candidate will develop:

• Net-aware models of multicommodity energy systems that explicitly account for distribution grid constraints;
• Optimization-based operational strategies for multicommodity energy systems, from an economic (energy market) and grid perspective, coordinating storage, conversion and flexible demand across multiple energy carriers;
• Framework to assess the effectiveness of net-aware multi-commodity energy systems in a regional context, considering regional energy demand and supply as well as the future effects of the energy transition on changes in demand and supply.

The developed models and strategies will be validated using real grid data and real operational conditions, with the TU/e campus serving as a full-scale validation environment. The results of this research will contribute to scalable, congestion-aware energy management concepts that support large-scale integration of renewable energy without immediate grid reinforcement.