PhD researcher: Smart active mobility to improve health and wellbeing

The PhD vacancy is part of the DynamiCity project, a Horizon Europe project that will demonstrate how urban transport systems can be transformed into net positive contributors not only to cleaner, better-connected cities, but also for possible cancer prevention and improved survivorship. The project advances a paradigm shift: transport is no longer a source of negative impacts – such as safety issues and adverse health effects, but rather a strategic means to promote healthy lifestyles and overall wellbeing. With this project, we aim to contribute to improving well-being and quality of life of people after cancer. The project will explore the role and function of active mobility (walking, cycling) and micro-mobility options (e.g., moped) to improve health and enhance sustainable mobility options.

The collaboration with different European cities in the project will make a lasting societal impact. The PhD research will involve actual applications, enabling you to really make a change in people’s life. In the Netherlands, the municipality of Helmond is our living lab.

This four-year PhD project is part of DynamiCity project and involves:

• Explore the role of active mobility and micro-mobility to improve quality of life, to support cancer survivorship, and to decrease the incidence of cancer or late effects from cancer.
• Design and specify smart mobility technologies and solutions that support active mobility and micro-mobility and can be integrated in the DynamiCity living labs, such as dynamic traffic light systems and app-infrastructure connections.
• Quantify the safety implications smart mobility systems for pedestrians, cyclists and micro-mobility users, and measure whether this changes how people travel.
• Support the municipality of Helmond in pilot implementations to enhance cycling, encourage more walking and reduce the life expectancy gap amongst neighborhoods through interventions.

EngD position: Design tool for tire-snow performance predictions

Background

Winter tires are characterized by excellent performance at cold temperatures and in snowy conditions. The development of new winter tires, however, can be challenging. For example, mold production is time consuming (due to all the small details – the so-called sipes – that are required for snow grip) and the quality and behavior of snow is highly variable, which is a challenge for design when comparing outdoor test results with laboratory conditions. Therefore, numerical simulation tools are being developed to better estimate the effect of design changes on the tire performances – especially relevant at the early stages of the development of new tires.

Approach

Although numerical simulation environments have been developed to predict the snow performance of newly developed tire prototypes, challenges remain in comparing and validating advanced numerical models, e.g., using the Arbitrary Lagrangian-Eulerian (ALE), Coupled Eulerian-Lagrangian (CEL), and Smoothed particle hydrodynamics (SPH) methods for snowy conditions. Furthermore, investigation on the speed, accuracy, and convergence of these models and integration within the simulation environment of Apollo is required to ensure the robustness of the tire prototyping process.

Final goal

The project aims to develop a comprehensive numerical model for tire-snow interaction: coupled numerical models for rubber-snow interaction will be implemented using the Abaqus software. The main objective is to select the most suitable solution, from the perspective of stakeholder demands, and implement and integrate it into Apollo’s professional environment. The accuracy for predicting snow performance of winter tires will be validated experimentally, followed by further model improvements, and implementation or design of a robust workflow and predictive design tool.

EngD position in Film Thickness measurement in Large bearings

Large Rolling Element Bearings are in use in many application such as trains, wind turbines, cranes, and other industrial applications. These bearings enable the rotation inside these systems, for example of the train wheels. If these bearings fail, this often means that the whole system cannot function anymore and must be repaired causing high costs and disruptions.

In this project a system will be designed which enables the measurement of the lubrication state inside grease lubricated bearings. The system will make use of electrical methods and can thus be applied to real bearings. With the help of this system it will be possible to improve and fine tune the life time models. Furthermore, they may enable users to actually base their maintenance on the real condition inside the bearing. This will allow the maintenance to be done at an optimal time and thus save material and make the systems more reliable. It also supplies a tool to study the lubrication of large bearings in more detail which can lead to development and optimisation of future systems.

The position will be embedded in the SKF University Technology Centre (UTC) on grease lubrication and is linked to the Surface Technology and Tribology and the Tribology Based Maintenance (TBM) chairs. Our enthusiastic group is highly international and has a long history of research in the area of tribology. Our research combines both experimental research as well as model development in diverse tribology and degradation related topics.

The implemented system relies on the electrical impedance of a lubricated contact. By using the capacitance of the system the film thickness can be determined. The electrical resistance can be used to understand lubricant changes as well as mixed lubrication conditions. An existing model can be used to infer from the measured total impedance to the impedance of all the inhomougenously loaded contacts within the bearing.

The design work in this project entails the technical steps:

• Mechanical and electrical implementation
• Development of an evaluation methodology
• Building and implementing in experimental bearing setup
• Validation, Reporting and Handbook writing.

The candidate is expected to plan and carry out their research in alignment with the project goal. They will report during progress meetings of our research group and with the project partner. The industrial partner will be actively involved in the project by discussions, performing analysis and experiments.

The university offers a very stimulating scientific environment and a dynamic ecosystem with enthusiastic colleagues in which internationalization is an important aspect of the strategic agenda.

Two PhD positions in the Department of Molecules and Materials on the Development of Degradable Polymers with In-situ Optica...

HyFINE is a national project uniting a consortium of knowledge institutes and businesses to make chemicals greener and affordable. We are shifting from fossil feedstocks like oils and gas to sustainable sources like waste, using clean energy like green hydrogen and renewable electricity. The goal is to create climate-neutral, circular production of ingredients for applications such as adhesives, paints, plastics and cosmetics. Beyond technology development, HyFINE focuses on training new experts and sharing knowledge to establish the Netherlands as a leader in green chemistry.(www.nwo.nl/en/news/193-million-euro-for-consortium-that-makes-special-and-fine-chemicals-more-sustainable)

As a PhD student within the HyFINE consortium, you'll be at the forefront of developing degradable polymers that may be used in a variety of consumer and industrial application. You'll leverage your expertise in organic chemistry to modify polymers using green chemistry and hydrogen/electrons/photons, enabling biodegradability. You’ll systematically study the impact of molecular structural parameters on material properties and biodegradability (‘structure-degradation correlation’). You’ll also optimize light source for the degradation, and develop in-situ optical sensing platform (e.g., UV-VIS and Raman spectroscopies) to monitor the degradation rate. You will work closely with industrial partners to source the starting polymers, have access to their biodegradation platform for testing modified polymers, and gain advice on targeting material properties of the polymers.

What sets this opportunity apart:

Real-world impact: Collaborate closely with academic and industrial partners, upscaling your synthesized materials, and building your sensing platform for real-world testing and fine-tuning your work based on industry feedback.

World-class facilities: Conduct your research at the University of Twente, a vibrant campus in the east of the Netherlands equipped with state-of-the-art facilities.

Expert guidance: Work alongside renowned experts Prof. Frederik Wurm and Dr. Ivana Qianqi Lin in the Sustainable Polymer Chemistry and Hybrid Materials for Opto-electronics group within the Department of Molecules and Materials, as well as industrial researchers in BASF and SULIS Polymers.

This is your chance to contribute to a more sustainable future while advancing your research career at a leading European university.

Join us in shaping the future of sustainable materials and technologies!

Postdoc position in open-source respiratory signal analysis (M3RESP)

You will work on the development of M3Resp, a new open-source FAIR platform for multimodal monitoring of respiratory signals in the Intensive Care setting, including surface EMG (sEMG), electrical impedance tomography (EIT), mechanical ventilator waveforms, and respiratory muscle pressures. This project is a close collaboration between the University of Twente (CRPH - Cardiovascular and Respiratory Physiology group, sEMG expertise center) and the Intensive Care department of the Erasmus MC (ROTARC - Rotterdam Advanced Respiratory Care group, EIT expertise center), two internationally leading institutes in respiratory and mechanical ventilation research. Open Science NL provides the funding to stimulate open, reusable, community-driven research software. Both institutions provide a full-time position each for the duration of 1 year, and both candidates will closely collaborate with each other and with data researchers in building a sustainable national digital research infrastructure.

By integrating and extending two existing research software packages (ALIVE and ReSurfEMG), you will enable unified data handling, signal processing, and user-friendly GUI-based analyses for clinical and physiological research.
You will combine Python-based software development, biomedical signal processing, and FAIR data design, and contribute to a platform that supports researchers in understanding and analyzing complex respiratory time-series data.

You will merge codebases, implement dedicated data containers, port and optimize signal processing algorithms, build a Dash-based GUI, and develop standardized analysis pipelines. The role includes community-oriented tasks such as documentation, tutorials, and user support. The work requires strong programming skills, experience with time-series data, and an interest in biomedical signals and open-source research software.