PhD position: The water footprint of European food: Towards water-efficient and resilient food production and consumption pa...

Agriculture is responsible for more than 90% of global freshwater consumption and the main driver of overexploitation of finite and vulnerable freshwater resources in many parts of the world. With the demand for food growing, geopolitical dynamics affecting food trade patterns, and climate change influencing the water cycle, it is imperative to make most of every drop while building resilience in the face of shocks and scarcity. In response, Europe strives to improve water efficiency and resilience of its food systems, both on the production and the consumption side (including imports). A major limiting factor, however, is that we do not sufficiently understand how much water is consumed in the production of food and other agricultural products. And once such water footprint accounts are available, how can European and national policy makers address inefficiencies, boost resilience, and mitigate water stress--both within Europe and in countries from which it imports its food?

The position
As part of a collaborative doctoral partnership between the European Commission’s Joint Research Centre (JRC) and the University of Twente (UT), this PhD position will bridge academic excellence with policy relevance to enhance the sustainability of water use of European consumption of food and other agricultural products within Europe and along global supply chains.

In the first two years, you will be positioned at the University of Twente in Enschede, the Netherlands and get introduced into the PhD trajectory and scientific working. You will alongside the developers of UT’s global crop water model ACEA (Mialyk et al. 2024) and improve the model’s ability to provide recurring updates of global crop water footprint accounts and automize key routines. Based on the resulting accounts, you will calculate water footprints of crop-derived products, such as food stuffs, bio-based materials, and bioenergy. Following the calculation of volumetric footprints of global food production, local consequences of water use need to be analysed. For this purpose, you will apply and enhance methods from both Water Footprint Assessment and Life Cycle Assessment perspectives (Berger et al. 2025). This may include developing approaches for matching the spatial and temporal resolution of LCA databases and impact assessment models or explore new ways to assess the impact of green water consumption.

You will spend the second part of the PhD trajectory (years 3 and 4) at the JRC in Ispra, Italy, where you will refine and test your models and methods in case studies of relevance for policy support. You will align your results and proposed methods with the European Commission’s Environmental Footprint and contribute to its continuous development and update. You will also get the chance to simulate and analyse alternative agricultural and food production scenarios in support of the transition to healthy and sustainable diets according to the most recent policy needs and developments.

PhD position on Sustainable Mobile Networks

Mobile networks, as a key component of digital connectivity and computing infrastructures, play a crucial role in modern life. With the growing urgency of sustainable design and operation of digital infrastructures, this PhD project will design and develop resource management schemes that enhance the sustainability and efficiency of next-generation mobile networks. The research will focus on optimising the use of network and compute resources in software-defined and virtualised environments, supporting the evolution toward a flexible edge-cloud continuum. The successful candidate will investigate intelligent orchestration and control strategies to enable sustainable mobile networks considering virtual network functions, telco-edge infrastructures, and emerging AI-driven user applications.

Key research directions include:

• Modeling and profiling of emerging AI-based workloads and data-intensive applications in mobile networks.
• Sustainable and energy-aware orchestration of softwarized network functions.
• Dynamic resource allocation and optimization across distributed cloud–edge infrastructures.
• Performance analysis in simulators and experimentation in a testbed.

The candidate will be working in the DACS research group under the supervision of dr. Suzan Bayhan and prof.dr.ir. Geert Heijenk. Moreover, the PhD candidate will be collaborating with other researchers and external partners.

Postdoctoral position: A CARtilage on CHIP model for precision medicine in osteoarthritis (CARCHIP)

CARCHIP will combine the expertise of the University of Twente (UTwente), chiron, Covaris, Stichting Proefdiervrij and the University of Maastricht (UM) to develop and implement a pioneering cartilage-on-chip model specifically designed for precision medicine in osteoarthritis (OA), the most common degenerative joint disease affecting more than 1.5 million Dutch citizens. To study OA patient heterogeneity at the molecular level, primary cells and tissues obtained from osteoarthritic patients will be endotyped using advanced mass spectrometry-based proteomics. Endotype specific primary cell cultures will be then established using animal free culture media and will be genetically modified with bioluminescent reporters for OA endotype in vitro modelling. These modified cells will be used to establish cartilage on chip cultures using a unique platform consisting of 24 cartilage on chip devices that can be simultaneously exposed to multidirectional loading mimicking cartilage forces in the moving human knee joint. We will validate this model by evaluating both established anti-inflammatory drugs and novel disease-modifying agents currently under development. Additionally, chiron will initiate essential steps toward the commercialization of this innovative platform, which has the potential to revolutionize the development of OA treatment strategies. By integrating advanced technologies such as MS and bioluminescent imaging with cutting-edge organ-on-chip models, the project aspires to enhance understanding of OA pathology and facilitate the development of targeted therapies. This approach not only aims to improve patient outcomes but also seeks to address the urgent need for more effective treatments for this prevalent condition which still cannot be treated effectively. Ultimately, the project will contribute significantly to the field of precision medicine, paving the way for personalized treatment plans that can better meet the needs of individuals suffering from OA, thereby reducing the overall healthcare burden associated with this chronic disease. Successful completion of the project will present the CARCHIP technology as a viable and more alternative for animal experimentation ultimately translating in a reduction of animal use..

EngD position: Data-Driven Reliability Analysis of Underground Infrastructure Maps (ZoARG EngD)

Background

The Netherlands operates a digital system for exchanging location data of infrastructure networks, such as underground cables and pipelines. This system (known as KLIC) is managed by Kadaster, while network owners are responsible for providing accurate data about the location of their buried assets.

Many utility maps used in construction projects combine outdated and recently surveyed network drawings. As a result, it is often unclear whether a map is up-to-date, complete, or accurate. To verify this, project teams dig trial trenches during construction and maintenance works to compare the actual underground situation with the map.

The Challenge
Contractors and municipalities hold hundreds of reports containing sketches and analyses of trial trenches. These often include comparisons between the actual cable positions and the official network maps (KLIC deliveries). A analysis of these datasets yield valuable insights into the conditions under which maps are more or less reliable. However, such an analysis has not yet been conducted. This leaves stakeholders unable to statistically and systematically assess the quality of official network maps.

Could this be done differently? Is it possible to systematically analyse all trench documentation using data-driven methods to better interpret the reliability of underground maps?

Your Role
As an EngD candidate, you will work under supervision to develop a data-driven model for assessing the reliability of official network maps (KLIC deliveries). You will build a prototype algorithm that can be used by infrastructure and civil engineering professionals to better evaluate cable and pipeline location data.

Your tasks will include:

1. Analysing existing trench datasets (content, format, resolution, completeness)
2. Defining the concept of ‘reliability’
3. Exploring suitable data mining and analysis methods
4. Collecting case data
5. Selecting predictive parameters
6. Training algorithms (e.g. decision trees, gradient boosting, neural networks)
7. Validating the prototype with end users

Your Team: ZoARG
This project is part of the ZoARG2.0 programme, in which seven major excavation chain partners (Alliander, Enexis, Gasunie, Heijmans, Kadaster, KPN, Siers, and Vitens) collaborate on research, development, and education in excavation damage prevention and responsible digging. Three of these organisations form the project steering committee.

Between 2025 and 2028, five EngD projects will be launched within ZoARG2.0. Candidates will work with UT researchers to advance scientific knowledge (digital technologies, process models, and training) aimed at improving safety in the excavation sector. ZoARG projects are carried out in close collaboration with industry partners, allowing candidates to develop both academically and professionally.

Start date: March 2026
Duration: 2 years