PhD position on the design and fabrication of MEMS drag force-based flow and fluid composition sensors

In this project, we will combine well-known thermal flow sensing principles with micromachined mechanical sensors that measure the flow through the bending or displacement of a mechanical structure. In this way, we expect that, in addition to mass flow, many relevant gas parameters can be measured, such as thermal conductivity, density, specific heat, and dynamic viscosity.

For the measurement of gas flows, (micromachined) thermal flow sensors are often used because of their high sensitivity, low pressure drop, and relatively straightforward operating principle. However, their response is dependent on the properties of the fluid, especially the thermal conductivity and heat capacity. Micromachined mechanical flow sensors, based on bending or moving microstructures, have been primarily used for specific applications, such as measuring turbulence, where small and fast sensor elements are required. However, their response is dependent on the viscosity and density of the gas. The combination of thermal and mechanical sensing elements in a single device will reveal valuable information on the gas properties and, therefore, on the composition of the gas.

Important drawbacks of mechanical sensors are the fragility of the sensitive mechanical structures and susceptibility to contamination like dust particles. These drawbacks are not present when operated inside flow channels with clean gases, which is the case for the applications addressed in this project. We will focus on two types of sensors:

• miniature probe sensors that can be placed inside larger flow channels, and
• sensors integrated inside microchannels fabricated in the so-called surface channel technology, which allows integration with thermal sensors and even micro Coriolis sensors on a single chip.

In the new project “Flow and fluid composition sensing using integrated drag force flow sensors”, new sensor designs as well as their cleanroom fabrication by silicon micromachining will be investigated. The main challenges are (1) the design and modelling of new sensor topologies, (2) development of the MEMS fabrication processes, (3) fabrication of the sensor chips in the MESA+ cleanroom and (4) evaluation of the resulting performance in flow and gas property sensing in our MEMS measurement lab. The project is part of the KIC FLOW++ programme “Break-through technologies in flow and fluid composition measurement”. It involves close cooperation with flow sensor companies and the TU Delft, where a post-doc will focus on the electronic interfacing of the sensors.

The PhD candidate will work at the Integrated Devices and Systems (IDS) group within the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente in Enschede, the Netherlands. You will carry out the research at the University of Twente, with guidance from senior scientists and support from a senior engineer. Various teaching activities in your field of expertise may take up to 20% of your time.

Postdoc position for 2 years.

To bring computing power to the edge and to make the cloud sustainable, various paradigms for energy-efficient computing are emerging. These paradigms, for example neuromorphic, wave-based, probabilistic, and in-memory computing, are based on a wide variety of physical processes, materials, architectures, and algorithms. For effective implementation, these aspects need to be mapped, integrated with current technology, and coupled to technological use cases.

In this two-year project, a postdoctoral researcher will join forces with academic and industrial consortium partners to map how emerging computing paradigms can be implemented. The results of the various research projects in the consortium and developments reported in the literature will be monitored and analyzed. Where feasible and opportune, proof-of-principle experiments will be conducted together with various consortium partners. You have a background in hands-on (wafer-scale) device fabrication, preferably based on processing of advanced materials.

EngD position: Accurate laboratory indicator for snow performance of tread compounds

During the early stages of developing winter and all-season tread compounds, it is essential to accurately predict their snow performance in the laboratory phase. This enables the proper selection of the most promising candidates for tire prototyping, which will later undergo outdoor testing to assess real-world snow performance.

Given the high costs associated with outdoor snow testing and the increasing impact of climate change, resulting in inconsistent temperatures and snow conditions that often fall outside acceptable testing parameters, accurate laboratory-based prediction methods have become even more critical.

The current laboratory method for snow performance prediction, which relies on Dynamic Mechanical Analysis (DMA), requires tread compounds to be heavily optimized for snow performance to ensure compliance with regulatory snow tests. However, this often leads to overengineering for snow conditions, potentially compromising other key performance areas, such as dry handling.

Therefore, there is a need for a more sensitive and balanced laboratory method for snow performance prediction; one that enables accurate evaluation without unnecessarily sacrificing other performance characteristics.

The aim of this project is to develop and validate a sensitive laboratory method for predicting the snow performance of winter and all-season tread compounds. The key objectives include:

• Development of a laboratory method
• Achieving a snow performance prediction accuracy of 95%
• Ensuring high precision of the method in terms of both repeatability and reproducibility
• The accuracy of the new laboratory method will be validated against the snow performance results of winter and all-season tires obtained through outdoor testing.

Assistant Professor in Stochastic Operations Research

We seek the most talented individual to strengthen our department with a focus on mathematical methods for Stochastic Operations Research, in particular on exact and approximate methods in Markov decision theory. Our new colleague is envisaged to work on applications in healthcare optimisation, but also expand the current strengths of the department in new directions. The position is embedded in both the Stochastic Operations Research team of Applied Mathematics and the Centre for Healthcare Operations Improvement and Research (CHOIR). We invite interested candidates to determine if the University of Twente is a potential match by visiting these websites.

As an Assistant Professor:

• You develop your own identity as a mathematician. You are expected to attract national or European research grants to support your research line.
• You will be involved in the teaching program of the Department of Applied Mathematics (DAMUT) at the Bachelor and Master level, and our department also supplies service teaching to other programs and faculties.
• The lectures are in English. Mastering the Dutch language is not a prerequisite, but naturally a long-term goal.
• Young staff members spend the first three years of their employment 33% on teaching and 67% research; later, the standard division is 50/50.

2 PhD Positions to contribute to a project crafting the future of healthcare decision making

In the section Health Technology and Services research at the University of Twente, we believe in making healthcare decisions that truly reflect the needs and values of patients. For that purpose, we are participating in a large international project dedicated to revolutionizing patient-centered decision making in healthcare with partners from different universities, industry and patient organizations. Within this project, we offer two PhD positions. The project contains different workstreams, and the PhD’s will be working along senior staff to perform tasks in different workstreams, in strong collaboration with multiple international partners and fellow PhDs from all over the world.

Key Responsibilities

Both PhD’s will conduct one high-quality systematic literature review on existing methods and/or applications to integrate patient preference information, clinical outcome assessments, and information from digital health technologies.

Each PhD will be involved in a case study where data on patient reported outcomes, patient needs and preferences, and clinical outcomes will be collected with digital health technologies, and analyzed using innovative statistical methods.

PhD project 1 will focus on systematic ways to engage patients and include their perspectives on health care innovation in a case study, but also on how to include different partners in the project and the development of a framework for obtaining a robust view of a health innovations full benefits to patients.

PhD project 2 will focus on the development and application of innovative methods for data-analysis and integration, including AI based methods to combine the different types of data, using data from different case studies.

Both PhD projects are closely related, and the PhD’s are expected to collaborate together and with PhD’s and project partners around the world.