PhD Candidate in Exposomics, Machine Learning and Artificial Intelligence

In this PhD project, you will design and apply innovative computational approaches, such as graphical models, penalised structural models, and large language models (LLMs), to investigate associations between disease outcomes and complex, high-dimensional, time-varying exposure data. You will integrate rich exposome datasets with multiple omics layers (including genomics, proteomics, metabolomics, and microbiomics) to uncover how environmental factors shape human health at the molecular level.

You will collaborate closely with leading experts in epidemiology, omics, and environmental health within the Gravitation program Exposome-NL, a national research consortium that brings together more than fifty scientists from diverse disciplines and institutions across the Netherlands.

PhD Position: Unlocking the potential of after school care

A growing number of children are enrolled in Dutch after-school care. They spend a considerable amount of time in these after school programmes, almost half of the time they spend in school. This suggests that these programmes can contribute significantly to children’s broad development. Especially with an increasing focus on academics in primary education, there is a great potential for after school programmes to foster other areas of development, including various interests, social-emotional skills, and citizenship to name a few. However, still little is known about the quality of after school care and how it relates to children’s well-being and their broad development. This PhD study aims to address this gap by evaluating the pedagogical quality of after school care and children’s experiences in this field. Key questions are:

• Which structural quality characteristics predict process quality? (Study 1)
• Which profiles of after school care programs emerge in Dutch practice and how are they related to children’s wellbeing and engagement? (Study 2)
• What is the children’s voice and their views and experiences in after school care? (Study 3)
• How do caregivers strike a balance of children’s individual needs in dyadic caregiver-child relations and the dynamics of the group? (Study 4)

The project involves:

• quantitative analysis of large-scale data (i.e., large-scale assessments from the LKK project) (Study 1 & 2);
• qualitative analysis of child data collected with the mosaic method (Study 3);
• a mixed-method analysis of the caregiver-child relationship from the perspective of staff and youth (Study 4).

You will use innovative observational methods and state-of-the-art statistical analyses to investigate significant predictors of the process quality of after-school care using existing, large datasets. The data includes:

• questionnaires for managers and caregivers;
• (coded) observational data;
• (coded) video data of interactions between staff and children, and (coded) video data of children (e.g., wellbeing and involvement in vivo).

The qualitative data may include children’s drawing, photovoice, talks and other qualitative data using the mosaic approach. There is also a possibility to develop new protocols for data collection and coding (Study 3 & 4).

PhD position in Neuroscience

As a PhD candidate in this project, you will use advanced imaging and molecular techniques to investigate defensive behaviors in larval zebrafish, providing new insights into the neurons and circuits involved. During your PhD you will develop a range of research-focused and broader academic skills, including:

• Volumetric imaging: perform experiments using a light-sheet microscope optimized for live, volumetric imaging of larval zebrafish.
• Data analysis: analyze whole‑brain calcium imaging recordings.
• Spatially resolved molecular methods: apply expansion microscopy (ExM) adapted for spatial transcriptomics to identify neuronal subpopulations underlying observed responses.
• Anatomical characterization: use transgenic lines to uncover morphological and neuroanatomical features of those neuronal subpopulations.
• Scientific communication: write manuscripts for publication, resulting in your PhD thesis and present your research at local and international conferences.
• Supervision and teaching: co-supervise undergraduate and graduate student projects and participate in undergraduate teaching.

PhD: Integrated Assessment Modelling of Climate Feedbacks, Tipping Points

Climate change is driven in part by complex feedback mechanisms within both natural and human systems. Some of these feedbacks can lead to so-called tipping points: critical thresholds beyond which the climate system may shift abruptly and potentially irreversibly. These processes are not yet fully integrated into the analysis of integrated assessment models (IAMs), which are used to support policymakers in designing effective climate strategies.

In this PhD project, you will work on improving the representation of feedbacks, tipping points, and extreme events in the MIMOSA model, a flexible IAM that links climate dynamics with socio-economic processes. You will develop an inventory of critical feedbacks, translate them into model representations, and conduct uncertainty analyses to evaluate their impact on cost-effective climate pathways. You will also explore the expansion of MIMOSA with a simplified energy system module and investigate linkages with the more detailed IMAGE model.

The project is embedded in the broader EMBRACER programme, which aims to advance our understanding of climate feedbacks. You will therefore collaborate with colleagues in climate science, systems modelling, and policy analysis.

Post Doc: Integrating biodiversity data into spatial energy planning

The BIOGAIN project (Enabling biodiversity-positive transformation of energy planning towards climate neutrality), funded through the Biodiversa+ programme, brings together leading universities and research institutes from across Europe to identify pathways for biodiversity-positive energy planning.

As a postdoctoral researcher, you will focus on integrating biodiversity data and spatial optimisation methods into renewable energy planning. This involves exploring how advanced data on nature and biodiversity (derived from AI-supported monitoring and analysis of sources such as satellite imagery, acoustic sensors, and camera traps) can inform spatial planning and decision-making for solar and wind energy infrastructures. Working closely with European partners in ecology, data science, and planning, you will assess biodiversity data availability and develop methods to spatially identify areas of synergy and conflict between renewable energy and biodiversity objectives.

Your tasks and responsibilities are:

• identifying and analysing essential biodiversity indicators relevant for spatial energy planning across different European contexts;
• screening planning documents and data sources to assess how biodiversity information is currently quantified and integrated;
• evaluating additional baseline data and explore how AI-supported biodiversity monitoring can strengthen planning evidence and decision support;
• developing and applying spatial optimisation methods to identify areas of synergy and trade-off between renewable energy development and biodiversity objectives;
• collaborating with consortium partners working on ecology, data science, and governance to ensure methodological coherence and integration across work packages;
• engaging with policymakers, developers, and other stakeholders to make biodiversity data and spatial tools accessible and useful for planning practice;
• publishing scientific papers and contribute to transdisciplinary outputs, stakeholder workshops, and dissemination activities within the BIOGAIN consortium.

While the tasks in the BIOGAIN project have to be completed as outlined, you get the flexibility to emphasise your own research interests and strengths. We will provide you with the conditions and support to advance in your academic and/or professional career. Resources are secured for participation in multiple international conferences, interaction on site with stakeholders in the Netherlands and participating in project meetings on site with our European partner organisations.