Postdoctoral Position in Protein Engineering for Cancer Therapeutics and Biosensor Development

We are seeking a talented postdoctoral researcher to engineer pore-forming toxins (PFTs) for targeted cancer therapeutics and biosensor development.

You will work on developing and optimizing pore-forming toxins (PFTs) for selective cancer cell targeting. This will involve expressing and purifying PFTs, as well as performing (computational) protein engineering and directed evolution to create PFTs that target cancer cells with high selectivity.

Since PFTs can also function as biosensing elements (as biological nanopores) you will also utilize these PFTs in artificial lipid bilayers for sensing applications and collaborate on the development of a microfluidic platform.

There will be room for you to contribute to shaping the specific approaches taken in this project, allowing you to tailor aspects to your own research interests, where feasible.

You will have access to state-of-the-art facilities and will work in a highly collaborative and supportive research environment, with many opportunities for interdisciplinary collaboration.

PhD's in “Advanced Wave Engineering for Sustainable Optical Applications (AWAVE)”

NWO-funded research program “Advanced Wave Engineering for Sustainable Optical Applications (AWAVE)” addresses optical challenges to enable high-tech optical devices that are energy efficient, secure, and preserve scarce resources, with the aim of rendering the worldwide urbanization sustainable. To realize future high-tech devices that are sustainable for billions of people, it is crucial to improve their optical functionalities and efficiencies. Remarkably, major relevant Dutch high-tech industries, semiconductor nanofabrication, printing technology, lighting, share common optical technology challenges for such devices. Our multidisciplinary team of AWAVE researchers will develop novel flat optics with reduced aberrations and dimensions, in close collaboration with users ACCHyperion, ASML, Canon, Demcon, JMO, SCIL, Signify, Sumipro to solve these challenges. AWAVE takes a major step forward by the unique combination of freeform gradient-index optics on the macroscale and metasurfaces on the microscale. See also the infographic at:

At the University of Twente, we look for PhD candidates for research projects entitled: Flat Optics Manufacturing; and Flat Optics Metrology for scattering.

PhD Position: Design and Manufacturing of Adaptive Winglets for Green Aviation

The Chairs of Nonlinear Solid Mechanics (NSM) and Advanced Manufacturing for Sustainable Products and Energy Systems (AMPSES), within Faculty of Engineering Technology, have an opening for a PhD position focused on the development of adaptive winglets using shape memory alloys in aircraft.

The Challenge
This research project seeks to advance the aviation sector by developing adaptive winglets featuring morphing surfaces, utilizing Shape Memory Alloys (SMAs) and a hybrid AI-driven control system. This approach aims to deliver notable aerodynamic gains, extending aircraft range and operational efficiency while lowering environmental impact. The control system combines passive mechanisms that respond to ambient temperature variations at different altitudes with active thermal adjustments for precise, real-time modulation. This dual-control strategy optimizes winglet configurations across various flight phases, reducing reliance on conventional, heavy mechanical components. By introducing this hybrid system, the project aims to establish a foundational shift in aircraft design prioritizing fuel efficiency, emissions reduction, and alignment with climate-neutral targets for 2050.

Your role

• Participate in cutting-edge research under the guidance of leading experts in the field.
• Engage in the design, simulation, and experimental testing of SMAs and morphing structures.
• Collaborate with an interdisciplinary team including members from the University of Twente, Technical University Delft, and other partners.
• Report your research findings in bi-weekly meetings and at international conferences.
• Prepare and publish your research in high-impact academic journals.

PhD position Digital Humanities, AI, and Citizen Science

We are looking for a talented and motivated PhD candidate in the field of Digital Humanities to research the motivation of citizen scientists to annotate textual and graphic patterns in digitized multimodal collections (e.g. handwritten archives). The candidate should also be able to evaluate and co-develop a web interface that ensures the appropriate division of labour between ‘humans in the loop’ and machines in the context of continuously learning Artificial Intelligence (AI)-driven systems.

Your research will help to create innovative feedback loops between engaged volunteers and machine-generated output: recurring textual/graphic patterns (e.g. headings, tables, marginalia) or visualizations of basic narrative components (e.g. place, circumstances of a historical event, thematic aspects) as can be found in many multimodal archive collections. Although there have been enormous developments in the field of automated handwriting recognition (HTR), problems at the level of layout analysis still need to be addressed . In order to exploit deep learning in these areas, it is necessary to have ‘humans/citizens in the loop’, who not only label characters and words and transcribe lines but also train machines to analyse images in a multifaceted manner.

Your work – together with the work of other researchers in Work Package 2 of the HAICu project (https://www.haicu.science/) – will contribute to new ways of multimodal data mining on the basis of layout and graphical attributes. As a PhD researcher, you will work within the HAICu research project. The HAICu (digital Humanities - Artificial Intelligence - Cultural heritage) project is a large-scale Dutch research project jointly pursued by universities and cultural-heritage institutions. We study new forms of Artificial Intelligence-based access to multimodal cultural-heritage data, both contemporary and historical. Within HAICu, Digital Humanities researchers, AI researchers, and a wide range of public and private partners will co-develop scientific solutions to unlock the true societal potential of heterogeneous digital heritage collections. It will provide easier, richer, and more reliable data access to citizens, journalists, civic organisations, and various other stakeholders.

Within the HAICu team, you will participate in Work Package 2, titled Continual Machine Learning and Humans in the Loop. In this Work Package, we will collaborate with AI researchers from the University of Groningen and NHL Stenden, and partners from Dutch cultural heritage institutions, such as the Nationaal Archief (Dutch National Archives), Collectie Overijssel, and Groninger Archieven. As a PhD student in the TPS department, you will collaborate with a team of supervisors, including Dr. Andreas Weber, Dr. Annemieke Romein, and Dr. Koray Karaca. At the UT, you will work in a vibrant environment with diverse research projects and opportunities for collaboration and exchange.

In addition to publishing scientific articles, the researcher is expected to make code available to academic and institutional project partners under open-access conditions.

For more information about HAICu, please see https://www.haicu.science/

PhD Fellowship: Computational Predictive Models of Skeletal Muscle Remodelling for Regenerative Robotics Applications

Join us at the Neuro-Mechanical Modeling and Engineering Lab, where we're pushing boundaries in muscle neurophysiology, biomechanics, and robotics as part of the ERC Consolidator Grant ROBOREACTOR.

This 4-year PhD position offers you the chance to work in an innovative interdisciplinary environment, collaborating on groundbreaking research at the frontier of healthcare and robotics.

Project Overview
As a PhD fellow, you’ll play a central role in building a predictive, multi-scale model of human skeletal muscle. This model will simulate how motor units within muscles respond to neural signals discharged by spinal neurons and adapt structurally over time when subjected to specific physical strain regimens. Leveraging machine learning and statistical modeling, you’ll integrate data from in vivo and in vitro studies to accurately predict muscle remodelling. The model will be validated against data from both healthy participants and post-stroke patients following a targeted 12-week leg training protocol. Using advanced tools such as high-density electromyography, ultrasound, and force dynamometry, you'll bridge biomechanics and neurophysiology, driving novel insights in muscle modelling and rehabilitation.

Key Responsibilities
As part of our team, you will:

• Develop a computational muscle model, particularly for leg muscles, that simulates biological remodelling over time based on strain stimuli.
• Use high-density EMG, ultrasound, and force dynamometry to personalize models to reflect individual neuromuscular physiology.
• Program model remodelling logics in languages such as C++ and Python.
• Train machine learning algorithms to identify the most probable muscle remodelling processes based on strain data.
• Validate the model with both healthy and stroke patients, as well as through in vitro muscle data.

Collaborate with experts in control engineering, robotics, and bioengineering to contribute to developing a rehabilitation robotic system capable of autonomous tissue regeneration.