PhD position in Healthcare Systems Modeling and Simulation

Join our innovative Health Technology and Services Research (HTSR) section at the Faculty of Behavioural Management and Social Sciences. We emphasize personal and professional development and encourage social activities. Contribute to future-proofing the healthcare system while maintaining accessibility and quality!

The Challenge

Our healthcare systems are facing growing and multifaceted challenges. The ever-increasing demand for care is outstripping capacity. At the same time, rapid technological advancements require constant adaption of intervention strategies, necessitating responsible use of our resources. These dual pressures highlight the need for innovative solutions to ease the burden on healthcare systems, optimize resource utilization, and enhance patient outcomes. Addressing these challenges requires the development and evaluation of strategies such as integrated care or personalised medicine that account for the complexity and dynamic nature of healthcare systems and their outcomes across various levels.

A highly motivated and skilled PhD candidate is needed to address crucial healthcare challenges using a systems approach, showcasing advanced simulation techniques. This position is ideal for someone with a strong foundation in healthcare systems and/or exceptional quantitative skills, who is driven to improve healthcare efficiency and sustainability.

In this research hybrid simulation model will be developed. This model should combine individual- and system-level elements. The main aim is to evaluate and optimize healthcare strategies through systems thinking. This could involve, for example, optimising cancer pathways through multidisciplinary care coordination.

The research will involve:

• Individual-Level Modeling: Using a bottom-up approach, patients will be simulated at the individual level, capturing unique responses to interventions and interactions with environmental and system resources.
• System-Level Modeling: At a higher level, parameters such as disease dynamics, population trends, and financial flows to provide a complete, system-wide perspective will be integrated.

This project requires partnering with healthcare organizations to make tangible impacts on healthcare delivery.

PhD Position on Operando characterisation of Solid Oxide and Protonic Ceramic Electrolysis Cells

High-Temperature Solid Oxide Electrolysers (SOEC) and Protonic Ceramic Electrolysers (PCEC) are promising technologies with the potential to reduce the electrical energy consumption by 30% compared to conventional low temperature electrolysers. The elevated operating temperatures, typically above 450°C, also allow for synergies with industrial production processes (e.g. steel, ammonia, etc.) where waste heat or steam is available. However, the high degradation rates during operation, particularly under intermittent loads, remain a barrier for accelerated scale-up and deployment of these technologies. Thus, more than ever, it is vital to get in-operando information of cell behaviour beyond the usual electrochemical performance measurements, to better understand the various degradation modes and propose design and control strategies to mitigate them.

As part of the ~50 Mio€ ‘HyPRO’ project, the largest ever R&D project on green hydrogen in the Netherlands bringing together 58 partners from research and industry, we are looking for a PhD candidate to develop and execute in-operando SOEC and PCEC characterisation studies. The candidate will design and construct a new cell testing setup, building off existing setups in our lab, that will allow single cell testing at high temperatures (450-1000 oC) and pressurized conditions (1-10 bar) while providing optical access, e.g. via a quartz/sapphire window, to the cell. Aside from standard current-voltage and EIS measurements, the setup should facilitate IR imaging and vibrational spectroscopy (e.g. Raman) to monitor in (close to) real-time thermal and compositional variations across the cell, including local anomalies such as hot spots, and cracks. Combined with pre- and post-mortem microstructural (FIB-SEM, XCT) and compositional (EDX, XRD, XPS) characterisation, these operando studies will help correlate operating parameters, i.e. applied current/potential, feed composition and flow rates, and temperature ramp rates and fluctuations, to cell degradation indicators, such as changes in electrode composition, triple phase boundary length, charge conductivity, etc. Powered by this understanding, the candidate will then be responsible for proposing cell design and control strategies (steam and air/power/thermal) to reduce cell degradation, especially under intermittent operation, and verify them over stability tests = 2000 h.

PhD position on Hardware Security

Public-key cryptography is vulnerable to mathematical attacks by quantum computers. Governments encourage organizations to adopt new post-quantum cryptography algorithms to secure critical infrastructure. However, these new algorithms are still susceptible to physical attacks, while current testing infrastructures to assess their resistance to physical attacks sacrifice quality for run time. This project investigates new methods inspired by genomics (to improve quality) in combination with digital hardware design (to reduce run time) to thoroughly evaluate the security of post-quantum protected implementations against deep-learning-based side-channel analysis.

We invite applications for a PhD position at the Computer Architecture for Embedded Systems group. We are looking for a talented and highly motivated candidate to work on this project doing research at the intersection of hardware security (specifically side-channel analysis) and FPGA design.

Post-doc research position “Magnetron Sputtering of Piezoelectric Thin Films”

The XUV Optics Group at Twente has started a new multidisciplinary research program on these topics. We develop forefront fundamental research, relevant to high tech applications Industrial Focus Group XUV Optics. The research will take place in a state-of-the-art thin film laboratory within the MESA+ Institute for Nanotechnology at the University of Twente, in collaboration with various academic and industrial partners.

Challenge
Your goal is to develop the key material science required for mastering magnetron sputtering of piezoelectric thin films. This includes:

• Exploiting new deposition schemes and parameters of reactive magnetron sputtering, aiming for optimal piezoelectric thin film materials performance.
• Developing the understanding of the piezoelectric film structures and their functional properties using characterization techniques such as AFM, XRD, SEM and DBLI and detailed analysis.

PhD Position on Digital twins for health monitoring and control of Anion Exchange Membrane (AEM) electrolysers

Anion Exchange Membrane (AEM) electrolysers aim to combine the best aspects of Alkaline and PEM technologies to achieve a lower cost of hydrogen production. AEM electrolysers use lower cost and more earth-abundant materials than PEM and, typically, have higher efficiencies than alkaline electrolysers. However, AEM electrolysers are prone to higher degradation rates, particularly the membrane, and are not yet able to match the current densities and, thus, H2 production rates of PEM electrolysers.

As part of the ~50 Mio€ ‘HyPRO’ project, the largest ever R&D project on green hydrogen in the Netherlands bringing together 58 partners from research and industry, we are looking for a PhD candidate to develop a hybrid physics-based and data-driven digital twin of an AEM cell. The goal of the digital twin will be to monitor the health and forecast the remaining useful life for predictive maintenance of an operating AEM cell developed by collaboration partner HyET E-Trol. A core feature of your work should be the inclusion of a neural network (NN) surrogate of a detailed physics-based and experimentally validated model of the AEM cell to provide access to unmeasurable internal cell parameters during operation. To twin the NN surrogate to a HyET E-Trol AEM cell in operation and track its health, recurrent neural networks, particle and/or extended Kalman filters, and inverse parameter estimation methods might be worth exploring. By uncovering the dependency between the cell health and the operating parameters, the remaining useful life for predictive maintenance and advanced control strategies to extend life under operation should also be explored. Finally, in addition to the modelling, you will also contribute to the experimental stability testing of the AEM cell at HyET E-Trol for data acquisition and model validation.