10 Postdoctoral Fellowship at University of Twente, Netherlands

Word jij enthousiast van het verbeteren en toepassen van AI bij het oplossen van klinische uitdagingen? Ben je communicatief vaardig en vind je het leuk om met mensen met verschillende achtergronden samen te werken? Als postdoc onderzoeker binnen de vakgroep M3i en het Rijnstate ziekenhuis maak je deel uit van het door ZonMW gefinancierde AI for EVAR-project, dat erop gericht is de zorg voor patiënten met een abdominaal aorta-aneurysma te transformeren. Je implementeert en valideert geavanceerde multimodal deep learning-modellen die beeldvorming en klinische data integreren om behandel en follow-up strategieën te personaliseren. Daarnaast voer je een vroege HTA analyse uit van de ontwikkelde modellen.

In Nederland wordt ongeveer 75% van de patiënten met een abdominaal aorta-aneurysma  endovasculair behandeld, waarbij een stent wordt geplaatst (EVAR). Hoewel EVAR goede korte-termijn resultaten oplevert, hebben deze patiënten op de lange termijn een verhoogd risico op complicaties en heringrepen. Dit maakt levenslange follow-up noodzakelijk.

Binnen het door ZonMW gefinancierde AI for EVAR-project ontwikkelen we multimodale modellen voor een optimale keuze voor behandeling en voor follow-up ná EVAR. Je implementeert en valideert multimodal deep learning-modellen die CT-beelden en klinische data combineren, getraind op de unieke RADAR-consortiumdatabase.

In this role, you will be responsible of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems.

In this role, you will contribute to the development and implementation of cutting-edge AI solutions for real-time image-guided medical applications. You will work hands-on with clinical data and build robust deep learning algorithms.

In this role, you will help develop and implement cutting-edge AI solutions for real-time, image-guided medical applications, with a focus on advanced robotics. You will work directly with clinical data to design robust, efficient deep learning algorithms that maximize the information extracted from images and delivered to the robot.

Are you excited about applying AI to solve real clinical challenges? As a postdoctoral researcher in the Mathematics of Imaging & AI (MIA) chair, you will join the ZonMW-funded AI for EVAR project, aiming to transform the care for patients with abdominal aortic aneurysms (AAA). You will develop and validate cutting-edge multimodal deep learning models that integrate imaging and clinical data to personalize treatment and follow-up strategies.

In the Netherlands, around 75% of patients with an abdominal aortic aneurysm (AAA) are treated via endovascular aneurysm repair (EVAR). While EVAR has good short-term outcomes, EVAR-patients have an increased long-term risk of complications and reinterventions. This requires life-long follow-up. In the ZonMW-funded AI for EVAR project, we develop multi-modal models for optimized selection of treatment before, and follow-up after EVAR.

You will implement and advance multimodal deep learning models combining CT imaging and clinical data, trained on the unique RADAR consortium database. These models will be validated in close collaboration with clinical and industrial partners, offering you the opportunity to translate AI innovations directly into patient care.

This project aims to revolutionize aircraft winglet design by developing morphing surfaces enabled by Shape Memory Alloys (SMAs). These adaptive winglets are designed to optimize aerodynamic performance by responding to temperature variations and incorporating active thermal control for real-time configuration adjustments. This hybrid passive-active system reduces dependence on traditional mechanical components, offering significant improvements in fuel efficiency, emissions reduction, and alignment with the aviation sector’s climate-neutral goals for 2050.

The Dynamics based Maintenance group, Faculty of Engineering Technology at the University of Twente, The Netherlands has an opening for a postdoc candidate within the project “Engineering for Zero Downtime in Cyber-Physical Systems via Intelligent Diagnostics (ZORRO)”.

To increase the availability and reliability of critical and complex high-tech systems, like machines used in the semiconductor industry or industrial printing machines, intelligent diagnostics to identify faults is of crucial importance. Acoustics-based diagnosis, using an array of microphones, is a promising method to identify faults, as sensing on the moving components in these high tech systems is not possible. The challenge is to localize anomalous sounds, related to faults, in a complex acoustic environment, characterized by moving sound sources and reverberations. Purely relying on physical models describing the acoustics in this kind of environment will result in models too large to be handled and too instable to be solved. Data-driven approaches need to be used in addition to enrich the physics-based models. An experimental set-up to collect relevant acoustic data is available, but needs further development.

The project focusses on a case of one of the participating industrial partners, but a broader collaboration in this national project with the other industrial partners, universities and research institutes is anticipated.

The logistics sector in the Netherlands is a vital economic pillar, employing over 673,000 people and contributing €65 billion annually. However, the sector faces pressing challenges, such as reducing greenhouse gas emissions, ensuring supply chain resilience amidst disruptions, and overcoming infrastructure and workforce shortages. With freight transport projected to grow by 20% by 2030, these challenges require a shift from isolated logistics operations to collaborative, connected logistics networks. Upcoming policy measures, including kilometre chargers, CO2 caps, the Emissions Trading Scheme, and the Corporate Sustainability Reporting Directive, add urgency to this transition.

A promising framework for addressing these challenges is the Physical Internet (PI), which envisions a transformative shift in logistics systems. The PI concept aims to “do more with less” by enabling the sharing of assets within the freight and transport industries. This involves transitioning from the isolated scheduling of proprietary assets to the collaborative scheduling of shared resources in open, connected logistics networks.

Staying in focus is a fundamental challenge in highly demanding optical systems where imaging plays a central role – from microscopes to telescopes, but also for advanced lithography. For the next-generation “hyper-NA” lithographic tool of ASML, the demands of keeping the wafer surface at the focal point of the imaging system will require innovative solutions to adapt the wafer surface position on the nm level! In order to have the wafer surface fully inside the depth of focus, height corrections of the order of 10-100 nm would be required, varying in absolute value over the surface of the wafer.

An elegant method to correct wafer position is to include an adaptive element into the wafer stage, which allows local modification of the surface height. A “form adaptive multilayer” would serve such purpose, where the local height of the multilayer should be able to be modified over the desired range. One method to achieve this is through thermal actuation, using a laser to locally heat the multilayer, where the applied heat creates a shrinkage (or expansion) of the heated materials. The challenge is to create such a “form adaptive multilayer”, matching industrial demands with scientific know-how, and thereby improving future lithographic applications!

We are looking for a motivated and skilled post-doctoral researcher to carry out advanced, thin film physics research on magnetron sputtering of piezoelectric thin films. Our aim is to develop understanding in relation between the magnetron sputtering parameters and the piezoelectric functional properties.