PhD position on model-based wavefront shaping microscopy

With wavefront shaping, you can focus light through non-transparent materials. Since the invention of wavefront shaping at the University of Twente, many applications have arisen, ranging from quantum security to microscopy.

The key challenge of wavefront shaping is to find out exactly how to shape the light to form a focus. Traditionally, this is done through iterative algorithms (‘trial and error’). In this project, we aim to develop a radically different approach where the correct shape is computed using a 3-D model of the non-transparent structure. Using this approach, we aim to build a microscope that can look inside very complex materials without loss of image quality.

Your role is to build flexible 3-D ray-traced models of the structures, based on STL files, 3-D voxel data and other input. You work closely with the rest of the team to integrate your models with the microscope hardware and GUI, with advanced machine-learning algorithms, and with lightning-fast Maxwell solvers for scattering simulations.

You will not only work on the 3-D models in theory; you will also be trained in operating advanced microscopy systems and directly test your models in experiments. Your research will help microscope users to see inside their samples without cutting them to pieces and thus have a direct impact on their daily work.

PhD position ‘Courage to Correct: Balancing Error Prevention and Learning in Strategic Crisis Teams’

In high-stakes crises, strategic teams often aim to avoid errors at all costs. Yet this drive for error-free performance at a group level can unintentionally silence early warning signals on a personal level-such as when conflicting information during a bomb-threat response go unchallenged, or when concerns about rising intensive care unit admissions are dismissed. These moments illustrate how hesitation to speak up can undermine adaptation and decision-making when it matters most.

Research shows that effective teams require psychological safety: the opportunity to voice concerns and seek feedback openly (Edmondson & Bransby, 2023). Studies on error management climates demonstrate that treating mistakes as learning opportunities enhances performance (Horvath et al., 2023). However, crisis contexts differ: what counts as a ‘mistake’ is often ambiguous in the moment. Rather than clearly identifiable errors, team members are frequently dealing with potential mistakes or contributions that may be perceived by others as inaccurate or inadequate. Voicing such concerns or alternative interpretations can entail personal and relational risks. Moreover, even perceived or potential errors may carry heavy consequences in crisis contexts, and recent work suggests that an exclusive focus on error management is insufficient. Instead, optimal performance emerges from balancing error management with error prevention (Van der Byl et al., 2023). Emerging theory argues for performance safety-the freedom to speak up combined with awareness of the responsibility errors entail (Taylor et al., 2025).

This reveals a core paradox for strategic crisis team decision making: teams striving to be error-free may unintentionally suppress the cues that help them learn, adapt, and intervene effectively. This tension between preventing errors and learning from them will be centre stage in this PhD project.

The activities include integrating literature on psychological safety, error climates, and crisis decision-making, and developing a conceptual framework for communication errors in strategic crisis teams. The project will collect interview data and analyse crisis scenarios to identify patterns of communication errors and their impacts. It will also test the effects of these errors and their consequences in controlled crisis simulations through field experiments combined with post-experimental interviews. Finally, interventions and training methods aimed at improving communication and reducing errors will be tested in controlled crisis simulations, again using field experiments complemented by post-experimental interviews.

This 4-year full-time PhD, based in the Psychology of Conflict, Risk and Safety section, focuses on strengthening communication, learning, and error response in strategic crisis teams.

EngD Position: LiDAR-Assisted Wind Field Forecasting for Next-Gen Turbines

About the Project

Wind energy is a cornerstone of the global energy transition, but increasing the sustainability of the turbines themselves is critical. The ECOWIND project focuses on an integrated strategy to extend operational lifespans and reduce the overall weight of wind turbines. By innovating ways to lower mechanical loads on critical components and optimizing material usage, we aim to pave the way for a truly circular wind energy sector.

Your Role
A key pillar of ECOWIND is bridging the gap between remote sensing technology and real-time turbine control. Your focus will be the development of a predictive capability that allows turbines to react to the wind before it hits the blades.

Using upstream LiDAR measurements (taken several rotor diameters ahead), you will develop a wind field forecasting method, leveraging principles like Taylor’s Frozen Turbulence Hypothesis, to estimate incoming turbulent flow. You will then assess the method’s validity under real atmospheric conditions using a combination of LiDAR data and Large Eddy Simulation (LES) results.

Key Responsibilities:

• Develop and refine numerical algorithms for real-time wind field forecasting.
• Validate forecasting models against high-fidelity LES data and field measurements.
• Quantify the impact of predictive adjustments on turbine performance and load reduction.
• Communicate findings through technical reports and presentations to both academic and industrial audiences.

Postdoctoral Researcher: High-Fidelity LES & Atmospheric Boundary Layer Modeling

About the Project

The ECOWIND project focuses on an integrated strategy to extend the operational lifespan and reduce the overall weight of wind turbines. By innovating ways to lower mechanical loads on critical components and optimizing material usage, we aim to pave the way for a truly circular wind energy sector. A key component of this mission is developing predictive "look-ahead" control capabilities based on LiDAR technology.

Your Mission: Advanced LES & Research Leadership
As a Postdoc, your primary focus will be the application and further development of open-source LES methods (e.g., OpenFOAM, SU2) to simulate a wide range of Atmospheric Boundary Layer (ABL) conditions.

Your work will provide the "ground truth" for the project. By simulating complex inflow conditions, you will create the high-fidelity datasets required to validate the Wind Field Forecasting (WFF) models developed within the team.

• LES Development & Application: Implement and extend open-source LES codes to simulate diverse ABL scenarios, including varying stability, shear, and turbulence intensities.
• Collaboration & Mentoring: Work closely with the project’s EngD researcher, providing the LES-generated data and expertise needed for the development of LiDAR-assisted forecasting methods.
• Methodological Innovation: Identify and implement improvements in LES sub-grid scale models or actuator disk/line representations to better capture turbine-atmosphere interactions.
• Scientific Impact: Lead the authoring of high-impact journal publications and present findings at major international conferences (e.g., Wind Energy Science, WESC).
• Consortium Engagement: Act as a key scientific liaison within the ECOWIND consortium, translating complex CFD results into actionable insights for structural and control engineers.

Researcher Digital Experimental Mechanics

We are looking for a Researcher to build up our experimental infrastructure and contribute to advancing mechanics education and research at UT. The role blends hands-on lab tasks, digital innovation, and joint research.

About the Role
As an Researcher in Digital Experimental Mechanics, you will contribute to modernising our experimental practice across education, research, and lab operations. You will help compose hybrid mechanics labs (hands-on + digital), develop reliable experimental workflows, support materials–surface interaction research, and contribute to long-term lab resilience. You will collaborate closely with academic staff, technicians, and researchers from Mechanical Engineering (ME), Industrial Design Engineering (IDE), and the wider Engineering Technology community. This position is ideal for someone who enjoys crafting things that work, making mechanics intuitive and accessible, and enabling others to do excellent experimental science.

What You Will Work On
You will play a key role in:

• Modernising Engineering Education
• Crafting hybrid learning experiences that combine physical experiments with simulations and datasets.
• Developing reusable, scalable teaching assets for mechanics and material-interaction courses.
• Helping students understand the link between materials, surfaces, and experienced performance.
• Building Digital & Data-Driven Workflows
• Developing standardised, traceable data acquisition and processing workflows.
• Contributing to an evolving “digital lab manual” with scripts, procedures, and guidelines.
• Improving reproducibility and efficiency in mechanical testing.
• Advancing Interaction Mechanics Research
• Supporting experimental campaigns on static and cyclic material–surface interactions.
• Collaborating on simplified digital twin models that connect experiments and simulations.
• Helping generate datasets that strengthen ongoing research lines.
• Strengthening Lab Capacity & Continuity
• Detailing critical experimental workflows and equipment procedures.
• Supporting training for staff and students.
• Contributing to a long-term continuity plan that ensures stable, future-ready laboratory operations.