Education Assistant for Practicals in chemical science 0,5-0,8FTE

Engaging practicals can motivate students for chemistry and chip technology and require good educational and technical support. We are looking for an employee for additional laboratory support to maintain the quality of the practicals with an increasing number of students, and to create opportunities for chip technology examples in the practicals. Your duties consist of varied activities within student laboratories, with a strong focus on chemical science and engineering.

You will be responsible for preparatory and concluding work related to teaching practicals; the logistics surrounding the laboratories so that the practicals run as efficiently as possible; and instructing and supporting teaching assistants and students during practicals.

In addition, you will be responsible for developing, managing, and maintaining the laboratory infrastructure in consultation with lecturers; contributing to the evaluation of practicals and providing suggestions for improvement; and contributing to activities and marketing materials that put the programmes in Chemical Science & Engineering and Advanced Technology on the map, such as open days, introduction days, and videos, particularly aimed at the chemical side of chip technology. This is done in collaboration with enthusiastic colleagues from other study programmes in a team that develops chip tech demo materials. This team also visits secondary schools.

Postdoctoral Fellow Opening: Reinforcement Learning for the Control of Human-Inspired Musculoskeletal Robots

As part of a SNSF Co-Investigator Grant between ETH Zurich’s SoftRobotics Lab and University of Twente’s NeuBotics Lab, we are seeking a highly motivated postdoctoral fellow interested in advancing reinforcement learning approaches for the control of human-inspired musculoskeletal robots. The project focuses on creating digital twins of musculoskeletal robots equipped with neuronal control networks, with the aim of deriving robust robot controllers for sim2real applications.

If you are excited by interdisciplinary research at the interface of robotics, biomechanics, artificial intelligence, and neuroscience, we encourage you to apply.

The opening
The project combines advanced neuromusculoskeletal modeling, reinforcement learning, imitation learning, and robotic experimentation to enable the next generation of human-inspired musculoskeletal robots.

Your tasks will be

• Gradually adapt human neuromusculoskeletal models to incorporate robotic limbs based on muscle-like, variable-stiffness actuators.
• Development of digital musculoskeletal robot twins integrating neuromusculoskeletal models and models of electrofluidic robotic actuators.
• Develop digital twins of musculoskeletal robotic limbs equipped with muscle-like actuators and neural control networks.
• Use reinforcement learning (RL) to train digital robot twins to learn roboust movements underlying human-like joint impedance control.
• Develop imitation learning frameworks where robotic limbs learn to reproduce human-like movement and stiffness properties by observing a moving human twin.
• Sim2Real transfer on RL-policies to real hardware.

Your secondary tasks will include:

• Collaboration with interdisciplinary researchers in biomechanics, robotics, and machine learning.
• Dissemination of research through publications, open-source software, and international conferences.

About the Lab
The NeuBotics Lab is a multidisciplinary team at the forefront of neuromechanics, robotics, and human movement science. Our work bridges neuroscience, biomechanics, artificial intelligence, and robotics to develop adaptive control strategies and real-time biomechanical models for assistive and autonomous robotic systems.

You will join a dynamic research environment focused on translating computational neuromusculoskeletal models into real-world robotic applications, including prosthetic limbs, wearable exoskeletons, and autonomous musculoskeletal robots.

Why Join Us?

• Be part of a cutting-edge research project.
• Collaborate with leading researchers across Europe.
• Work in a vibrant academic environment at one of the Netherlands’ top technical universities.
• Access to state-of-the-art lab computational and robotics facilities.

EngD position for AI-agent development for personalized osteoarthritis support

The Engineering Doctorate (EngD) programme Business & IT (BIT) offers you a two-year position combining education at the University of Twente with a design project carried out in close collaboration with Ancora Health.

The educational programme will have an in-depth and broadening character, with ample attention for professional development. It will be partly tailored to the design project. The EngD is a two-year post-master's design programme focused on the direct needs of industry. It combines an educational component with a design project in a professional context.

About the project
LoaD is an NWA-ORC funded project that studies osteoarthritis (OA) from genetics to multimodal behavioural and physiological data. This multidisciplinary approach aims to improve understanding of disease progression and enable better management through personalized digital support.

Within this EngD project, the focus is on developing and implementing an AI agent for people with knee OA. Rather than building a generic chatbot or static recommender, you will work on an agentic product concept that can interpret relevant patient data, reason over personalized goals and context, and generate timely recommendations, coaching prompts or actions within a mobile app.

The design challenge is to translate research insights, clinical requirements and software constraints into a coherent product that can be implemented in Ancora Health's mobile application. The first use case is knee osteoarthritis, but the architecture should be designed with broader applicability in mind, for example for other lifestyle-amenable chronic conditions where digital coaching, self-management and personalized feedback are relevant.

As an EngD, you will:

• Translate an agentic framework into a concrete product design for a personalized digital companion in the mobile app.
• Design and prototype the software architecture, user flows and interaction logic needed to implement the AI agent in a safe and usable way.
• Work with researchers, clinicians, product developers and end-users to align scientific insights, clinical requirements, technical feasibility and product priorities.
• Support the integration of monitoring data, recommender logic, coaching strategies and agentic reasoning into a coherent product concept.
• Evaluate the usability, feasibility and implementation requirements of the AI agent in a real-world mobile health context.

EngD position: Designing a Context-Aware AI for Automated Building Permitting

Building permit assessment remains a major bottleneck in accelerating new construction and renovation projects needed to meet energy transition goals and growing market demand. While efforts have been and are being made to transition the highly manual process to a more digitally supported workflow, most developments focus on the formalization of the rules and regulations and formal compliance checking using semantic methods. However, these developments fail to account for the highly interpretive nature of often ambiguous and sometimes contradictory national and EU regulations. To account for the interpretative and context-sensitive nature of building rules, it is important to adopt a comprehensive approach to automating compliance checking. Such an approach should determine how such rules are interpreted and operationalized in each specific context and try to support the decision-making based on that context-specific interpretation.

The Challenge
The aim of this project is to design a hybrid Artificial Intelligence (AI) platform that integrates codified regulatory knowledge (across regional, national, and EU jurisdictions) with data-driven analysis of previous permit applications and associated decisions. This integrated approach aims to establish the foundational infrastructure for automated, context-sensitive compliance assessment. Particular emphasis will be placed on scenarios where identical regulations are interpreted differently depending on regional or local context. The objective is to leverage AI techniques to learn mappings between contextual attributes (e.g., geographic location, project characteristics) and likely interpretative outcomes.

Context and Supervision
The project is sponsored by the EU (OIVA project) and the Joint Innovation Centre between TNO and the University of Twente. The consortium involves a number of municipalities in the Netherlands that will provide expertise and support (in the form of data and feedback).

You will be supervised by:

• Dr. Farid Vahdatikhaki (University of Twente)
• Dr. Erwin Hofman (University of Twente)
• Dr. Joao Santos (University of Twente )
• Mr. Irfan Pottachola (TNO)

You will work closely with TNO and municipalities to deliver a tailor-made design artifact with real-world impact. The project will start in August/September 2026 and is based in Enschede/Hengelo.

PhD Position in Tire Tribology: Micro-Abrasion of Advanced Tire Tread Materials

The Surface Technology and Tribology group within the Department of Mechanics of Solids, Surfaces and Systems (MS3), part of the Faculty of Engineering Technology (ET), is seeking a highly motivated PhD candidate to investigate micro-abrasion and wear mechanisms in tire tread materials. The position is funded by the Dutch Research Council (NWO) through the Open Technology Programme (OTP) and is part of a larger project of 2 PhDs and 1 EngD.

Tire wear is a major environmental challenge, contributing to microplastic pollution due to the persistence of rubber particles. Addressing this requires a deeper understanding of tribological mechanisms, particularly at the tire–road interface. This is critical for developing tire materials that balance performance, durability, and reduced environmental impact.

This PhD project focuses on understanding and modelling micro-abrasion in tire tread materials. It will develop wear mode diagrams for rubber compounds and study how composition affects transitions between abrasive wear modes. The work includes the development of experimental setups, laboratory validation, and microscale analysis using a single asperity tribo-setup, combined with ploughing modelling. The outcomes are expected to contribute to the development of more durable and sustainable tire materials.

You will work within the Surface Technology and Tribology group at the University of Twente, an international and collaborative environment with strong expertise in tribology, and surface and interface engineering. The group provides a stimulating research atmosphere with regular discussions and opportunities for interdisciplinary collaboration. The project is conducted with industrial partners, offering direct exposure to real-world challenges in tire wear and material performance.