Doctoral Candidate Spatial statistics for integrating IoT field sensor data and remote sensing data for nature-inclusive sol...

The University of Twente, Faculty ITC, wishes to increase the number of women in the faculty to have a more balanced staff profile. During all phases of the selection process, we will therefore prioritize selecting women who fit the profile.

Your challenge
The Dutch government, through the Ministry of Education, Culture and Science, has responded to the current global environmental challenges by establishing sector plan positions in critical scientific domains. At the Department of Environmental Resources, one of our activities is to address these challenges by developing and applying Geostatistical models for bridging knowledge gaps, data scarcity and uncertainty gaps, and governance gaps related to monitoring the environment on which humans depend.

A part of this is **spatial statistics of sensor data integration for nature-inclusive solutions **for monitoring stress and diseases of tree crops**.** Tree crops like cocoa, apart from their direct economic functions for smallholder farmers, sit at the intersection of many beneficial ecological functions (including carbon sequestration and cultural identity). However, these functions are threatened by environmental stressors and diseases such as Cocoa Swollen Shoot Virus (CSSV) disease, which depend on the complex web of interactions between and within above-ground and below-ground biotic and abiotic factors. The prevailing data and methodological gaps that have perpetuated knowledge gaps in the spatial and spatiotemporal patterns of tree disease, and the widened governance gaps of farms, have motivated this topic.

You will develop spatial statistical methods to integrate ground-based IoT sensor data, remote sensing data, and in-situ data for mapping the spatial trends of cocoa diseases. You will be involved in setting up an IoT sensor network in cocoa farms in Ghana. You are expected to address data integration challenges, including (1) spatial misalignment of networks, (2) temporal misalignments of observations, (2) probabilistic or likelihood misalignments, and (3) data quality issues, such as uncertainties in measurements, sparsity of network coverage resulting in small N, _missing data _resulting from malfunction of sensors, and outliers. For the purposes of evaluating model transferability, you will make a comparison with other economically important tree crops in food forests in the Netherlands. You will design a measurement setup for cocoa trees and review the wide range of applications of IoT sensors, their uncertainties, and the observable variables above and below ground that are important for predicting tree crop diseases and stresses. You will also explore simulation scenarios to evaluate the impact of indigenous and formal farming management practices on plant diseases.

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.