EngD position: Standardizing Fieldwork and Data Management for Utility Mapping

In this EngD project, you will design a standardized protocol for fieldwork and data handling of GPR measurements at the University of Twente’s Utility Mapping Site (UMS). The UMS provides an ideal environment for testing and evaluating utility detection technologies. While various GPR systems exist, a method for systematically collecting, documenting, and comparing measurement data is lacking. You will outline measurement procedures, key metadata, and performance indicators to maintain consistent measurements across different systems and environmental settings.

By following this protocol, you will conduct measurement campaigns and help develop a benchmark dataset. This dataset supports the evaluation of GPR systems used in utility mapping and future data-driven innovations in that field.

Your environment
This project is part of the ZoARG programme, a collaborative initiative aimed at minimizing excavation damage to underground infrastructure in the Netherlands. You will work within a multidisciplinary environment that includes:

• The University of Twente’s Departments of Civil Engineering and Management (CEM) and Applied Earth Sciences (AES)
• The Utility Mapping Site (UMS) at the UT FieldLab
• Industry collaborators involved in the ZoARG programme

What you will do

• Develop a consistent approach for conducting Ground Penetrating Radar measurements at the Utility Mapping Site
• Build a data management protocol for the collection, storage and documentation of GPR measurement data
• Establish pertinent measurement conditions and metadata needed to conduct detailed comparison of GPR systems
• Plan and complete repeated field measurement campaigns using different GPR systems
• Develop and sustain a well-organized benchmark dataset for utility mapping research
• Work in close partnership with infrastructure owners, contractors, technology providers, and researchers engaged in the ZoARG programme
• Report findings and translate results into practical recommendations for measurement practice and technology evaluation

EngD position: AI for Underground Infrastructure Detection and Characterisation

In this EngD project, you will develop an AI model that automatically detects underground infrastructure in GPR radargrams and estimates its depth. The project builds on the growing availability of high-quality GPR data collected at the University of Twente’s Utility Mapping Site (UMS), a unique test environment for utility mapping technologies.

Current machine learning models and their training data are limited in size, comprehensiveness, and realism – resulting in partial automation with limited performance. This constrains their usefulness in real-world conditions. Your challenge is to develop and validate machine learning models using systematically collected and accurately annotated GPR datasets. By combining geospatial data, subsurface sensing, and AI, you will contribute to the next generation of utility mapping technologies and support safer excavation practices.

Your environment
This project is part of the ZoARG|ReDUCE programme, a collaborative initiative aimed at minimizing excavation damage to underground infrastructure in the Netherlands. You will work within a multidisciplinary environment that includes:

• The University of Twente’s Departments of Civil Engineering and Management (CEM) and Applied Earth Sciences (AES)
• The Utility Mapping Site (UMS) at the UT FieldLab
• Industry collaborators involved in the ZoARG programme

What you will do

• Analyse existing GPR interpretation methods, machine learning techniques, and relevant software tools
• Explore and evaluate AI approaches for automated utility characterization
• Prepare, preprocess, and manage large GPR datasets collected at the Utility Mapping Site
• Design, develop, train, and validate machine learning models for interpreting GPR radargrams
• Compare developed models with existing approaches reported in literature and commercial software solutions
• Work in close partnership with infrastructure owners, contractors, technology providers, and researchers engaged in the ZoARG programme
• Report findings and translate results into practical recommendations for measurement practice and technology evaluation

PhD positions in Structural Health Monitoring of Welded Thermoplastic Composite Assemblies

Thermoplastic composites are widely regarded as promising materials for the next generation of commercial aircraft, combining excellent mechanical performance with low weight. In addition, their melt-processable matrix enables automated, high-rate manufacturing of components that can subsequently be assembled into complex aerostructures using welding. This provides significant opportunities for more efficient and cost-effective aircraft manufacturing.

However, welded composite assemblies are challenging to inspect using conventional non-destructive inspection techniques. As a result, larger safety margins are often required in structural design, leading to heavier structures, while maintenance intervals may be more conservative than necessary. Developing reliable methods to continuously assess the structural condition therefore enable both lighter designs and more efficient maintenance strategies.

To address this challenge, the project aims to develop structural health monitoring technologies based on a digital twin. The digital twin will combine information from the physical structure with models and monitoring data to assess its current structural state and predict its remaining lifetime. This will enable the condition of welded structures to be monitored throughout their service life, allowing maintenance to be planned when it is actually needed rather than according to predetermined intervals. Ultimately, this approach aims to contribute to lighter, safer, and more sustainable aircraft structures.

The PhD positions

Experimental characterization and digital twin development
A key challenge in developing a reliable digital twin is accurately characterizing the static and fatigue behaviour of welded thermoplastic composite joints. Since there are currently no well-established standards for fatigue testing of these joints, the project will involve developing experimental methods to reliably characterize their mechanical performance and damage evolution.

The experimental results will be used to develop constitutive models for the welded interface. In particular, these models should describe progressive interfacial damage development as a function of fatigue loading. The models will be implemented and validated in commercial finite element (FE) software. The resulting FE model will define the digital twin of the welded structure and provide the basis for the second PhD project, which will use the digital twin together with monitoring data to develop prognostic structural health monitoring strategies.

In this project you will:

• Perform experimental characterization of the static and fatigue performance of welded thermoplastic composite structures.
• Develop constitutive models that accurately describe the performance of the welded interface.
• Implement the developed models in commercial FE simulation software for the development of a digital twin and validate their accuracy against experiments.

We are looking for a colleague who has experience in mechanical experimentation of the fatigue behavior of composite materials and/or polymers, and is able to develop and implement constitutive models in FE software.

Development of a Structural Health Monitoring system
The ability to estimate the current state of the welded thermoplastic composite joint and the development of this state over time, is of decisive importance for lifetime performance modelling. The key challenges are the robust integration of a sensor system in the structure and the analysis of measured signals, which are typically strongly affected by environmental and operational conditions. This project aims to tackle these challenges by using piezo-electric and/or optical fiber based sensor system, combined with physics informed data analysis method, exploiting the digital twin model developed by the first PhD project.

In this project you will:

• Implement an effective sensor integration method for welded thermoplastic composite structures, using piezo-electric sensors, fiber optical sensor, or a combination of both.
• Perform dynamic experiments of pristine and (gradually) damaged structures to collect data for the state estimation methods.
• Develop signal processing methods to estimate, enriched by physics-based information, the current state of the welded structure and its development under fatigue loading.

We are looking for a colleague who has experience in sensor integration and dynamic experimentation, knowledge of piezo-electric or optical fiber based measurements, and proficiency in signal processing methods enriched with physics-based information.

PostDoc position on Wafer Bonding Physics for 3D Chip Integration

Wafer-to-wafer bonding is a key enabling technology for future 3D chip integration, where chips are stacked and interconnected to create faster, more compact and more energy-efficient semiconductor devices. However, the detailed mechanisms that determine bonding quality are still not sufficiently understood. In particular, the interplay between surface properties, wafer morphology, and the resulting bonding performance is still largely optimized empirically.

In this postdoctoral project, you will develop and validate experimental methods to study wafer bonding in a quantitative and physics-based way. You will work on wafer bonding measurements and combine these with advanced surface metrology. It is relevant to have experience with analysis techniques such as AFM, WLI, contact-angle, XPS, infrared metrology.

You will work in the XUV Optics group at the University of Twente, embedded in the MESA+ Institute for Nanotechnology. The project offers a combination of hands-on experimental work, data analysis, physical interpretation and close interaction with industrial partners. You will contribute to a scientifically challenging and industrially relevant topic, with the aiming at predictive relations between measurable wafer surface properties and bonding performance for next-generation 3D chip integration.

2 PhD positions on Measuring the Future of Power Electronics

The challenge:
Power electronics are central to renewable-energy systems, electric mobility, industrial electrification and modern power grids. New wide-bandgap semiconductor devices based on silicon carbide (SiC) and gallium nitride (GaN) can switch faster and reduce conversion losses, but their performance is increasingly difficult to measure with sufficient bandwidth, accuracy and traceability. When converter efficiencies exceed 99%, even very small measurement errors can obscure the losses that designers need to understand.

Within the EU Chips Joint Undertaking project Moore4Power, the University of Twente will develop new measurement sensors and methods for evaluating advanced power-electronic devices and converters. We are recruiting two PhD researchers who will work as a closely connected team. One position focuses on creating wideband current-sensing technology; the other focuses on sensor characterization and on electrical and calorimetric efficiency measurements.

Choose the research direction that fits you:
PhD 1 on Wideband current sensors has the primary emphasis of: electromagnetic sensor design, modelling, prototyping and experimental validation. The best fit is A candidate interested in high-frequency measurement techniques, sensor hardware development and precision experimentation.

PhD 2 on electrical and calorimetric efficiency measurement has the primary emphasis of: measurement-system design, sensor characterization, uncertainty analysis, converter testing and calorimeter development. The best fit is a candidate interested in measurement science, power conversion, thermal engineering and system-level power electronics evaluation.

PhD 1: Wideband current sensors for fast-switching power electronics
Fast SiC and GaN switching events occur on extremely short timescales. Measuring them demands sensors with exceptional bandwidth and minimal influence on the power circuit. In this PhD project, you will design, build and validate a new generation of wideband current sensors that can reveal switching behaviour and losses that are difficult to observe with today’s instrumentation.
Your research:

• Develop current-sensor concepts targeting a bandwidth above 300 MHz and an insertion inductance below 1 nH.
• Model the electromagnetic behaviour, parasitic effects, transfer characteristics and limitations of candidate sensor architectures.
• Design and fabricate sensor prototypes, including their mechanical integration, shielding, connections and readout interfaces.
• Develop experimental methods to determine amplitude response, phase response, time-domain behaviour, linearity and sensitivity to operating conditions.
• Characterize and calibrate the sensors in collaboration with VSL, the Dutch National Metrology Institute and a Moore4Power partner.
• Demonstrate the sensors in representative SiC and GaN switching circuits and quantify how sensor performance affects switching-loss evaluation.
• Publish the scientific results and translate promising concepts into practical measurement solutions for project partners.

PhD 2: Traceable efficiency measurement using electrical and thermal methods
For a converter operating above 99% efficiency, the losses are small compared with the power being processed. Quantifying these losses reliably therefore requires more than a conventional power measurement. In this PhD project, you will create and validate a measurement framework that combines wideband electrical measurements with a next-generation calorimeter. The result will be a powerful way to understand where losses occur and how confidently they can be measured.
Your research:

• Characterize wideband voltage and current sensors in both the frequency and time domains, including amplitude, phase, linearity, dynamic behaviour and environmental influences.
• Develop calibration and uncertainty-evaluation methods for using these sensors in electrical power and efficiency measurements.
• Apply the measurement system to investigate switching behaviour, switching losses and overall efficiency of SiC- and GaN-based converters developed at the University of Twente and by Moore4Power industrial partners.
• Design, build and validate a next-generation calorimeter with a novel thermal measurement approach for accurate loss determination.
• Combine and compare electrical and calorimetric results to identify systematic effects, improve confidence in the measurements and establish traceable validation routes.
• Optimize the methods for realistic converter operating conditions and communicate practical guidance to academic and industrial users.
• Publish the scientific results and demonstrate the methods on relevant power-electronic hardware.

What the two PhD researchers share:

• You will form a closely collaborating research team: the sensor concepts from PhD 1 will inform the measurement applications in PhD 2, while the validation needs from PhD 2 will guide sensor requirements and characterization.
• You will work at the interface of measurement science and power electronics, with access to expertise and laboratory facilities at the University of Twente and VSL.
• You will collaborate intensively with academic and industrial partners across Europe, including regular project meetings, technical exchanges and work visits.
• You will have the freedom and responsibility to shape your research direction, develop advanced experimental skills and contribute to solutions with clear scientific and industrial relevance.