Post-doctoral Researcher on Sustainable Consumer Behavior

At the VU Amsterdam FSH Department of Communication Science, we are looking for a Post-doctoral Researcher to join our team to work on a project on sustainable consumer behavior. The project, CIVIC-4CE, explores how people can help make the circular economy part of everyday life.

Many consumers want to extend product lifespans but face barriers such as cost, convenience, lack of access or lack of trust. The project studies what motivates circular choices and, together with businesses, creates new services, digital tools, and policies that make repairing and reusing accessible and desirable. By connecting behavioral research with product design, business models, and governance, CIVIC-4CE brings all actors in society together to build a practical and lasting transition toward a circular system that reduces waste and preserves value for society.

Your duties
The Post-doctoral Researcher will work on a work package within the CIVIC-4CE project that focuses on understanding what psychological, social, and contextual factors and specific beliefs most strongly drive Dutch consumers’ willingness to perform circular behaviors, such as repair or re-use, for the sector of electrical household equipment. You will work directly with Guido van Koningsbruggen and Ana Isabel Lopes. The behavioral science insights from this work package will form input for other projects within CIVIC-4CE.

The Post-doctoral Researcher is expected to conduct literature reviews, design qualitative and quantitative surveys (cross-sectional and longitudinal), collect and analyze survey data, report and communicate results, and participate in consortium activities in the context of the larger project. This involves presenting at conferences and workshops, and collaborating on academic and professional publications.

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

PhD in Optical Characterization and Photonic Performance Analysis of Liquid Crystal Polymer Coatings

You will:

• characterize one-way transparent cholesteric liquid crystal polymer films and switchable photonic coatings;
• measure angle-dependent reflection, transmission, haze, scattering, polarization selectivity, and spectral bandwidth;
• perform UV-Vis-NIR spectroscopy, polarized optical microscopy, ellipsometry, Mueller matrix analysis, and imaging-based optical measurements;
• evaluate RGB projection quality, image contrast, colour fidelity, modulation transfer, and stray-light suppression;
• study optical performance under external stimuli such as humidity, temperature, electric fields, bending, and repeated switching cycles;
• develop standardized measurement protocols for optical alignment, sample handling, measurement geometry, and data reporting;
• collaborate with researchers working on material synthesis, coating fabrication, simulations, and device integration;
• contribute to the optical validation of demonstrator devices such as smart visors, AR glasses, and privacy-protective optical displays;
• publish results in scientific journals and present your work at international conferences.

PhD in Device Integration of One-Way Transparent Optical Films for AR and Smart Visor Demonstrators

You will:

• integrate liquid crystal polymer optical films onto transparent substrates, visors, and AR-type display components;
• develop lamination, bonding, encapsulation, and packaging strategies that preserve optical performance;
• align projection optics with cholesteric reflection bands to optimize image brightness, contrast, colour fidelity, and viewing comfort;
• evaluate device-level performance, including one-way visibility, outward light suppression, transparency, haze, angular response, and image quality;
• design and build demonstrator devices combining optical films with micro-projectors, micro-LEDs, waveguides, or other compact display modules;
• perform environmental and mechanical testing, including bending, abrasion, humidity exposure, thermal cycling, and long-term stability;
• collaborate with materials researchers developing the films and with optical/perception experts evaluating performance;
• support translation toward scalable, manufacturable optical devices.

PhD in One-Way Transparent Liquid Crystal Polymer Films for Optical Interfaces

You will:

• design and formulate cholesteric liquid crystal polymer systems for one-way transparency;
• fabricate thin optical coatings using photo-alignment, spin coating, blade coating, lamination, and photopolymerization;
• develop multilayer RGB-selective films with controlled pitch, handedness, bandwidth, and angular response;
• characterize liquid crystal phase behaviour, molecular alignment, pitch uniformity, and film morphology;
• optimize optical performance, including transparency, reflectance, haze, polarization selectivity, colour fidelity, and viewing-angle stability;
• collaborate with experts in optical characterization, modelling, device integration, and perception studies;
• translate material-level insights into design rules for scalable one-way transparent films;
• publish scientific papers and present results at international conferences.

PhD in Explainability and Humans in the Loop in Optimization Processes

This PhD project is a part of the CoRDS project – Confident Data-Driven Decision Support (https://www.cords-dn.at). CoRDS is a large-scale Doctoral Network, funded by the European Union under the Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks programme Grant Agreement No. 101227512. It brings together 8 European universities and 14 societal partners in an ambitious interdisciplinary collaboration. Our shared mission is to advance the research on data-driven optimization methods and train a new generation of experts skilled in the combination of Operation Research and trustworthy Machine Learning. CoRDS goes beyond the state-of-the-art DDO methods by proposing decision support frameworks that combine OR and ML methods and enable robust, transparent, and fair solutions that reflect user preferences and address complex, uncertain real-world scenarios.

This PhD position belongs to the work package WP6: Transparency. As part of this doctoral project, you will develop novel knowledge representation techniques, algorithms for human-in-the-loop optimization, and interactive tools that combine graphical and natural language interfaces to gather and incorporate expert knowledge. A central research question is how human mental models of an optimization problem align or conflict with what optimization algorithms need, and how explanations can mediate that gap across the full optimization cycle.

Objectives
(1) Characterize the types of domain knowledge that human decision-makers hold about optimization problems, and formalize a representation that is both computationally tractable and interpretable by non-technical experts.
(2) Develop a bidirectional HITL framework where explanations mediate knowledge injection before optimization and feedback integration after, with explicit models of when and how human input should be weighted against algorithmic outputs.
(3) Evaluate the framework on trust calibration, knowledge elicitation consistency, and solution quality across at least two optimization domains and two human roles, studying whether explanations help humans decide when to intervene versus when to defer to the algorithm.
(4) Investigate the generalization of the proposed methods across optimization problem classes, and characterize the conditions under which human-in-the-loop interaction improves or degrades solution quality.

Expected Results
(1) A typology of domain knowledge types relevant to optimization settings, accompanied by a knowledge representation formalism that is feasible to integrate with optimization algorithms at different stages of the solving process.
(2) A set of algorithms for integrating domain knowledge before optimization as priors and after optimization in the form of structured expert feedback, with theoretical and empirical analysis of the conditions under which each integration mode is most beneficial.
(3) A validation framework assessing the internal consistency of elicited knowledge, the fidelity and utility of generated explanations, and the trade-off between solution optimality and human acceptance across different problem settings and user profiles.
(4) A tool with an interface combining graphical and natural language interaction to gather domain constraints and expert feedback in a structured way, tested across at least two domains and validated with users from different roles.

This position will be based in the Information Systems (IS) Group at the Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology (TU/e). The candidate will be supervised by Dr. Isel Grau and Dr. Yingqian Zhang (from TU/e). As part of the project you will also take part in two secondments, one at a industry partner, where you will test early prototypes in real logistic scenarios, and another at the University of Vienna, where you will collaborate with other PIs and PhDs of the project.

Note: Strong applications will be reviewed on a rolling basis. We encourage candidates to apply early and not wait for the deadline.