Logo Universiteit Twente

Vacatures geplaatst door Universiteit Twente

Mimir verzorgt het geautomatiseerde beheer van vacatures op vacaturebanken voor Universiteit Twente.

Laatste vacatures

PhD Candidate Geo artificial intelligence for mapping vegetation dynamics

The University of Twente wants to be an organisation that effectively deploys diversity, talents, and capabilities in the labour market for now and in the future. In the framework of our diversity and inclusiveness policy, we strongly stimulate people with a (work) disability to apply for this position.

The University of Twente is committed to providing a working environment where everyone is valued, respected, and supported to progress. Our priority is to ensure that no one is disadvantaged based on their ethnicity, gender, culture, disability, LGBTQ+ identities, family and caring responsibilities, age, or religion. We encourage everyone who shares these values to apply.

Your challenge
You will help us realizing the ambitions of the EARTHONE (Environmental Analysis and Resilience for Transformative Human-Optimized Natural Environments) Horizon Europe project from a geospatial perspective.

EARTHONE will contribute to optimizing the net removal of Greenhouse gas emissions by assessing the contribution of sustainable land-use changes in different environments. To achieve this, EARTHONE will follow a multidisciplinary and multiscale approach, integrating innovative data-driven technologies with regional context through a series of living labs in Southern Europe.

You will conduct a comprehensive vegetation seasonality (phenology) study at regional and local scales to better understand how vegetation responds to climate change by integrating and analysing climate simulations, weather and remotely sensed products, and ground observations collected from the existing networks and the project living labs. You will start by mapping the main phenological regions in Europe. Then you will focus on modelling the role of extreme weather events and land use change on vegetation seasonality and efficiency. For this, you will develop and apply geo-artificial intelligence methods, including spatiotemporal machine learning techniques, and gather and integrate experienced knowledge from climate modellers, foresters, soil scientists and agronomists.

0 sollicitaties
0 views


11-02-2025 Universiteit Twente
PhD position in the MCSA-DN “VoCS" - Detection and perception of smiled speech

The PhD student will investigate how to detect, quantify and qualify smiled speech and how the perception of it affects social interactions. To that end, an audiovisual database of smiled speech will be collected to enable studies into the detection and perception of smiled speech, investigating its acoustic and perceptual characteristics (in healthy subjects and Parkinson’s patients), and investigating the influence of smiling on impression formation. Secondments will take place at Audeering (Germany) and Maastricht University. We are seeking a cross-disciplinary PhD candidate with a background in Computer Science/Computational Linguistics/Digital Phonetics and an interest in speech communication science, human technology interaction, and machine learning. You will be working at the Human Media Interaction group in which computer science meets social science to investigate, design, and evaluate novel forms of human-computer interaction.

9 sollicitaties
0 views


07-02-2025 Universiteit Twente
PhD Candidate Disaster responses in near real time from UAV and SAR satellite data

The University of Twente wants to be an organisation that optimally deploys diversity, talents, and capabilities in the labour market for now and in the future. In the framework of our diversity and inclusiveness policy, we strongly stimulate people with a (work) disability to apply for this position.

The University of Twente is committed to providing a working environment where everyone is valued, respected, and supported to progress. Our priority is to ensure that no one is disadvantaged based on their ethnicity, gender, culture, disability, LGBTQ+ identities, family and caring responsibilities, age, or religion. We encourage everyone who shares these values to apply.

Your challenge
Natural and human-made disasters can cause widespread devastation in urban areas, impacting thousands of lives and resulting in extensive property damage. In densely populated areas, rapid and accurate assessment of structural stability, i.e. the stability of man-made structures like bridges and buildings, is important for effective emergency response. Providing reliable recommendations is critical to ensure swift, informed decisions to minimize harm and expedite recovery. These should help first responders to make the decisions on where and when to prioritize their activities. Careful monitoring and dedicated analysis offer the proper background to guide such response strategies, ultimately saving lives and reducing overall damage.

Multitemporal Synthetic Aperture Radar (SAR) images contain important information well-established for structural stability or health monitoring. Such technologies, like SAR interferometry (InSAR) and SAR tomography (TomoSAR), can provide information on urban structure elevation and subtle deformations or stability issues. This information, however, is often sparse in time or space as it relies on identifying coherent scatterers. The use of AI and quantum computing to integrate SAR-derived products with real-time UAV optical data can offer reliable recommendations to prioritize areas where first responders are needed.

The HURRICANE project, funded by Horizon Europe, focuses on real-time intelligence for crisis and natural emergencies. It brings together scientists and partners from a multidisciplinary consortium of 15 partners and it addresses the limitations hindering the adoption of innovations in the field of real-time awareness. One of the project's primary objectives is to combine space-based SAR data with real-time UAV optical data to provide reliable near-real-time recommendations for first responders. Addressing this research question will be the main objective of this position.

You are encouraged to conduct interdisciplinary and innovative research in the field of SAR remote sensing and machine learning. Additionally, you will attend project meetings, liaise with project partners and the public, and contribute to the education programs at ITC.

The project is set to commence on January 1, 2025, and will run for four years. You will be expected to develop Python-based automation code to generate near-real-time recommendation maps by the end of December 2027.

15 sollicitaties
0 views


04-02-2025 Universiteit Twente
Postdoc Allocation of healthcare capacity to diseases

We are looking for a highly motivated, enthusiastic, and curious postdoctoral researcher to join our innovative Health Technology and Services Research (HTSR) section at the Faculty of Behavioural Management and Social Sciences. In this dynamic and inclusive section of around 35 researchers personal and professional development is central and there is plenty of room for social activities. With this position you can contribute to making the healthcare system future-proof, while keeping it accessible and of high quality.

The challenge

The increasing demand for healthcare services is placing pressure on our healthcare system. In order to make healthcare future-proof we have to focus on how technology and organization of healthcare can improve the efficiency of healthcare. Current health technology assessments, however, evaluate health outcomes and costs of the implementation of technology, but do not take into account the impact of technology on required healthcare capacity (staff of various professions). Given the anticipated future shortages in capacity, this impact is highly relevant to evaluate. Hence, estimates of required capacity per disease are needed to allow prediction of future capacity requirements and to assess the impact of technology on capacity use.

We are seeking a postdoctoral researcher who wants to work at the intersection of healthcare, technology and society. This requires a deep understanding of healthcare and solid quantitative skills. In this 2-year postdoctoral position you will work together with various organizations, such as the National Institute for Public Health and the Environment (RIVM) and the Dutch Healthcare Authority (NZA), on a project that strives to:

  • Develop and refine methodologies to estimate the required healthcare capacity for diseases.
  • Apply the developed approach and integrate the results into a health economic evaluation model.
  • Evaluate the impact of technologies on future capacity.
  • Produce high-quality research outputs, including peer-reviewed publications and presentations at conferences.

6 sollicitaties
0 views


31-01-2025 Universiteit Twente
PhD on Harmonic Losses in Power Transformers

The Power Electronics Group within the Department of Electrical Engineering has a vacancy for a PhD researcher (fully funded, 4 years) to work in the European Partnership on Metrology joint research project ENSURE - Metrology for Electric energy and supply reliability. The focus of the ENSURE project is to perform the metrology research necessary to support the reliability of our electricity grids.

Power transformers and other grid components are suffering from the lower grid power quality caused by renewable generation. In strong collaboration with utilities and power transformer manufacturers, you will research the impact of grid harmonics on the losses of power transformers. Through your research, new insights in harmonic losses of power transformers should be achieved, that will support manufacturers to design and build the next generation of low-loss power transformers.

The goal of the PhD position is to model, measure and analyze the impact of harmonic losses in power transformers. Your R&D work involves the development of a frequency-dependent analytical model for harmonic losses in power transformers with different core magnetic materials (CRGO steel, amorphous metal core, etc.) and different winding configurations. This model should consider the total losses including the copper (load loss) and core (no-load loss) losses. To verify and improve the modelling, you will build lab-scale transformers and perform extensive systematic experiments on the total harmonic loss of the constructed transformers. ML/AI tools such as KANN (knowledge-aware artificial neural networks) will be used for model improvement as well as for proposing scaling laws for real power transformers.

9 sollicitaties
0 views


27-01-2025 Universiteit Twente