EngD position on Implementation and Testing of Virtual Knowledge Graph Infrastructure

The University of Twente and the Netherlands’ Cadastre, Land Registry and Mapping Agency (Kadaster) set up a collaboration project to advance the existing data infrastructures so they can support the extended application of analytical algorithms over authoritative data. The project team is seeking an Engineering Doctorate (ENGD) candidate with a genuine interest in inter- and cross-organisational data integration. Your work will result in designing and implementing (virtual) knowledge graph (VKG) infrastructures that allow efficient data querying without data replication. A challenge is enabling performant (spatial) querying (spatial access) across such data sources.

The project team will help you gain the required methodological and technical knowledge to conduct the project, which includes design science, knowledge graphs, semantic technologies, ontologies, and natural language processing.

The Netherlands’ Cadastre, Land Registry and Mapping Agency – in short, Kadaster and the University of Twente (UT) have joined forces to operate at the forefront of knowledge about federated data; the goal is to advance the research field and develop methods and techniques to extract, combine, and analyse information from distributed data sources, while accounting for the principles of ethical conduct, scientific integrity, and open science, to benefit the society.

To realise that goal, Kadaster and UT work together on the UTKa Datalab project under the umbrella of the Centre for Security and Digitalisation (CVD), a collaborative knowledge centre based in Apeldoorn, uniting educational institutions, businesses, and public organisations to address challenges in security and digital transformation. The project aims to work on trusted federative data infrastructures based on knowledge graph (KG) technology and simultaneously explore the potential of mutual augmentation of AI (particularly LLMs) and KG for Land Administration applications. The project will be conducted by a scientific team from Kadaster and UT, where five young researchers will join the team as EngD and PhD candidates, including the candidates employed through this call.

From the UT side, two departments of Geo-Information Processing (GIP) from Faculty ITC and Semantics, Cybersecurity & Services (SCS) from Faculty EEMCS will supervise the PhD and EngD candidates. The supervisory team will also include colleagues from the Kadaster Data Science team.

About Kadaster
Kadaster collects and registers administrative and spatial data on property, rights, and assets such as ships, aircraft, and telecom networks, ensuring legal certainty. As the responsible body for national mapping, maintaining the national reference coordinate system, and advising on land use and spatial data infrastructures, Kadaster provides information primarily through online services to civil-law notaries, local authorities, businesses, financial institutions, and individuals. As the custodian of the Key Registers Cadastre and Topography, Kadaster performs its public tasks transparently and in service of society. For further information about Kadaster check: https://www.kadaster.nl/ & https://labs.kadaster.nl/about/

About GIP
The GIP department at the UT’s Faculty of Geo-Information Science and Earth Observation (ITC) focuses on creating actionable geo-information for diverse stakeholders. GIP addresses critical societal challenges by designing methods to process heterogeneous spatio-temporal data and developing open geo-information solutions. Their interdisciplinary approach combines Geographic Information Science, Remote Sensing, Computer Science, and Digital Humanities, emphasising co-creation with domain experts to ensure societal relevance and scientific validity.

EngD position on LLM Inference for Knowledge Graph

Artificial intelligence (AI) and knowledge graphs (KGs) have become transformative technologies across numerous domains, enabling advanced decision-making, data integration, and knowledge extraction. AI, particularly large language models (LLMs), excels in natural language understanding and reasoning, while KGs provide structured, interpretable representations of data that enhance the contextual relevance and reliability of AI systems. Their combination paves the way for impactful innovations in various fields, including healthcare, urban planning, environmental monitoring, and public governance.

Research at the intersection of AI/LLMs and KGs is critical for addressing complex, data-driven challenges, especially in land administration, where reliable and interpretable data is paramount. Organizations like the Netherlands’ Cadastre, Land Registry, and Mapping Agency (Kadaster) rely on structured data to maintain legal certainty and provide insights for spatial planning and property rights. Integrating KGs and LLMs can enhance the utility of Kadaster’s datasets, enabling more advanced applications such as multi-modal data analysis, (geospatial) reasoning, and the development of trustworthy AI tools for land administration.

Some of the topics the EngD will investigate include:

• User query interpretation
• Knowledge graph prompting
• Dynamic knowledge fusion

About the project
The Netherlands’ Cadastre, Land Registry and Mapping Agency – in short, Kadaster and the University of Twente (UT) have joined forces to operate at the forefront of knowledge about federated data; the goal is to advance the research field and develop methods and techniques to extract, combine, and analyze information from distributed data sources, while accounting for the principles of ethical conduct, scientific integrity, and open science, to benefit the society.

To realize that goal, Kadaster and UT work together on the UTKa Datalab project under the umbrella of the Centre for Security and Digitalisation (CVD), a collaborative knowledge center based in Apeldoorn, uniting educational institutions, businesses, and public organizations to address challenges in security and digital transformation. The project aims to work on trusted federative data infrastructures based on KG technology and simultaneously explore the potential of mutual augmentation of AI (particularly LLMs) and KG for Land Administration applications. The project will be carried out by a team of five researchers (2 PhD candidates and 3 EngD candidates, including this vacancy) under joint supervision by Kadaster and UT.

The supervision team includes the Geo-Information Processing (GIP) department from UT Faculty ITC, Semantics, Cybersecurity & Services (SCS) department from UT Faculty EEMCS, and the Kadaster Data Science team.

About Kadaster
Kadaster collects and registers administrative and spatial data on property, rights, and assets such as ships, aircraft, and telecom networks, ensuring legal certainty. As the responsible body for national mapping, maintaining the national reference coordinate system, and advising on land use and spatial data infrastructures, Kadaster provides information primarily through online services to civil-law notaries, local authorities, businesses, financial institutions, and individuals. As the custodian of the Key Registers Cadastre and Topography, Kadaster performs its public tasks transparently and in service of society. For further information about Kadaster, please check the following websites: https://www.kadaster.nl/ & https://labs.kadaster.nl/about/.

About GIP
The GIP department at the UT’s Faculty of Geo-Information Science and Earth Observation (ITC) focuses on creating actionable geo-information for diverse stakeholders. GIP addresses critical societal challenges by designing methods to process heterogeneous spatio-temporal data and developing open geo-information solutions. Their interdisciplinary approach combines Geographic Information Science, Remote Sensing, Computer Science, and Digital Humanities, emphasizing co-creation with domain experts to ensure societal relevance and scientific validity.

PhD opening in Understanding the impact of nature-based solutions in small rivers

Alongside two other PhD students at the University of Twente, you will be participating with many other researchers and practitioners in the national growth fund program: NL2120, elevating nature-based solutions. Recently funded by the Dutch government, NL2120 aims to develop the knowledge that is essential for supporting the large-scale realization of nature-based solutions.

This PhD project aims to explore the impact of nature-based solutions on small river systems in the Netherlands. Nature-based solutions promote the utilization of natural processes to create a resilient water infrastructure that simultaneously satisfies societal and ecosystem needs. Such nature-based solutions include creating space for water retention and periodic flooding, re-meandering of straightened river sections, removing bank protection, increasing flow resistance by promoting vegetation growth, and installing natural structures such as dead wood, which increases the flow variability and provides habitats for a wide range of species. Although these measures are expected to help prevent floods and/or droughts and to promote ecosystem services, they are being implemented on “ad-hoc” designs without knowing the long-term and larger-scale developments and their contribution to the desired impacts.

The River Dinkel and possibly other small rivers in the vicinity of the UT will be used as case studies to investigate how recent nature-based interventions have affected the hydro- and morphodynamics of these small rivers. The research will be carried out by a combination of small- and large-scale hydrodynamic and morphodynamic modeling to evaluate the current situation and to investigate the future development of the system. Developing these models (either from scratch or by adapting existing models) will be part of the project. Available field data (e.g., river bathymetries and cross-sections, discharges, etc) will be complemented by flow velocity measurements with acoustic instrumentation (ADCPs and ADVs) to support the model studies. The goal of this research is to assess the impact of different nature-based designs and to develop tools to generalize the knowledge from the case studies.

PHD-opening for Environmental Accounting for Nature-Based Solutions in Environmental Policy

Nature-Based Solutions (NBS) are a key topic in climate policy, but their role, especially in offsetting schemes, remains debated. For instance, bioenergy and tree planting are often treated inconsistently in carbon accounting, highlighting the need for more coherent frameworks. Emerging policies like the EU Carbon Removal Certification Scheme and the Paris Agreement’s Article 6.4 lack clarity on NBS implementation, and efforts like the "Trillion Tree Campaign" have faced criticism for their weak monitoring and diverting attention for much needed reduction of fossil fuel emissions and reducing deforestation. This 4-year PhD, part of the NL2120 Growth Fund Program, will explore the accounting challenges in NBS, including the integration of biogenic carbon, biodiversity, and co-benefits like water quality and availability.

Your research will focus on Environmental Accounting for Nature-Based Solutions by:

1. Evaluating Existing Accounting Frameworks: Assess how biogenic carbon and biodiversity are accounted for in policies (e.g., the Paris Agreement, Nature Restoration Law)
2. Investigating Key Accounting Paradigms: Determine how impacts of NBS are accounted for in LCA, corporate accounting and integrated assessment models, and derive recommendations for improving Life Cycle Assessment (LCA) methods.
3. Assessing specific NBS with LCA (based on 1 and 2): Conduct an LCA analysis for NBS in the Netherlands for example regarding wetland restoration.
4. Critical perspective on NBS: Determine the risk of mitigation deterrence and potential conflicts with agriculture or housing development.

You will work closely with a multidisciplinary team from academia, consultancy, and government to ensure the research is both scientifically rigorous and policy-relevant.

PhD position on Human-Robot Interaction and AI For Health Coaching

Healthcare systems are transforming due to demographic changes, economic pressure, and epidemiological shifts, among other factors. This creates a need for innovative and supportive solutions such as (social) robots, chatbots, and intelligent virtual avatars. These technologies can potentially support staff and improve people's lives by promoting healthier lifestyles, providing patient care, and enhancing rehabilitation outcomes.

Advancements in (conversational) AI and large language models (LLMs) have made interactions with these systems more seamless and intuitive. However, these technologies must offer personalized and sensitive guidance while addressing potential risks to support vulnerable user groups.

Besides natural language interaction capabilities, there is a growing need to enhance robots' physical interaction skills with patients, enabling them to replicate the supportive roles of physiotherapists and caregivers. This involves fostering social-physical contact, boosting adherence to rehabilitation programs, and dynamically adapting to users' changing goals and preferences.

We aim to develop adaptive, long-term coaching robots that integrate conversational fluency, human-like physical interaction, and interactive learning to deliver personalized and effective support in coaching and rehabilitation scenarios for enhancing health and well-being.

We are looking for a dedicated researcher with expertise in AI, robotics, and NLP to join us in advancing this field. You will lead the way in designing and developing personalized physical Human-Robot Interaction methods and innovative machine-learning solutions, helping redefine the future of coaching, rehabilitation, and care. The specific Ph.D. research topic is open to the candidate's background and interests.