Finding structure in multi-modal food data with visual analytics
KU Leuven, Belgium
- Institution
- KU Leuven
- Country
- Belgium
- Subject area
- Computer Science & AI
- Location
- Leuven, Belgium
- Funding
- Fully funded position
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About this position
The PhD candidate will join the Augmented Intelligence for Data Analytics (AIDA) research group, led by Prof. Jan Aerts, within the Department of Biosystems, KU Leuven (Belgium). The AIDA-lab develops methods at the interface of unsupervised machine learning, topological data analysis, and interactive data visualisation, with the goal of embedding computational analysis into expert workflows across life sciences, agriculture, and food systems. The candidate will apply this approach within a large collaborative project on healthy and sustainable food.
Project
Despite growing awareness on sustainability and healthy diets, dietary patterns only improve slowly due to a lack of incentives and facilitators for consumers. Information is fragmented, labels are often confusing, and food environments frequently make healthy and sustainable choices difficult. At the same time, food producers and retailers struggle to communicate sustainability efforts in ways that are meaningful and trusted by consumers.
The HSFood4All project addresses these challenges by:
- Integrating environmental, nutritional, social, and economic data.
- Developing innovative multi-dimensional food labelling and communication tools.
- Studying consumer behaviour, food literacy, and food environments.
- Testing behavioural interventions and communication strategies.
- Co-creating solutions through citizen research labs and stakeholder engagement.
- Delivering evidence-based recommendations for industry and policymakers
- Discovery: what patterns hide across the facets? - Using unsupervised machine learning (clustering, dimensionality reduction), topological data analysis and interactive visualisation in a tight loop with domain experts, surface structure and cross-domain relationships (nutrition–environment trade-offs, consumer segments, cross-facet dependencies) that siloed analyses cannot reach. Which methods reveal which structure, and where they agree or diverge, is open.
- Integration: how do you make heterogeneous data speak to each other? - Sources differ in resolution, structure, and completeness, and the choice of representation (relational, document or graph database) determines which cross-modal structure can be used for downstream unsupervised analysis. Designing that data representation (schema) and the integration strategy over joint consumer/product data is a research problem in its own right.
- Translation: what makes a visual design effective? - At a later stage in the project, we will design front-of-package (FOP) visual formats at multiple levels of informational detail grounded in perception and cognition, so that consumers and policymakers can make data-informed decisions. What encodings stay faithful to the data while remaining interpretable to non-experts is an empirical, testable question.
Profile
- EU Master Degree in Data Science, Bioscience Engineering, Computer Science or a closely related field
- Demonstrated understanding of unsupervised machine learning (clustering, dimensionality reduction); exposure to topological data analysis is welcome.
- An exploratory, investigative mindset: comfortable working without a fixed hypothesis, iterating on both analytical methods and visual designs.
- Solid proficiency in Python; knowledge of SQL (NoSQL experience (document and graph databases) is an asset).
- Interest in visual design and communication of data, beyond default plotting tools.
- Strong analytical skills and interest in interdisciplinary, applied methodological research.
- Knowledge of Dutch is a plus.
Offer
- A one year position that can be extended to four years after positive evaluation. Possibility to enroll in the PhD programme of the Arenberg Doctoral School.
- Supervision by an internationally recognised and highly interdisciplinary research team.
- Opportunities to collaborate with leading academic, industry, and policy stakeholders.
- Access to advanced training programmes, international conferences, and career development opportunities.
- A dynamic, international, and supportive working environment.
- The opportunity to contribute to research with significant scientific and societal impact.
Interested?
KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.
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