Business Developer Deeptech

Wat ga je doen?

Je werkt in het hart van innovatie. Jouw missie: het scouten van kansrijk onderzoek en dit transformeren naar impactvolle businesscases.

• Scouting & Screening: Je trekt de universiteit in om innovatieve deeptech (zoals fotonica, quantum en nanotechnologie) met marktpotentie te identificeren.
• Begeleiding: Je coacht onderzoekers en uitvinders bij het creëren van maatschappelijke impact en het verkennen van de commerciële route.
• Netwerken: Je vertegenwoordigt het KTO in het nationale Thematische Technology Transfer (TTT) programma Deeptech.
• Samenwerken: Jij bent de spil in een krachtig ecosysteem. Je schakelt moeiteloos tussen collega-business developers, het IP-team, juristen, de holding van de universiteit en Novel-T. Hoewel je autonoom werkt aan je eigen doelstellingen, ben je de drijvende kracht binnen multidisciplinaire teams waar expertise en ondernemerschap samenkomen.
• Marktanalyse: Je creëert een 'pull-effect' door continu op de hoogte te blijven van marktvragen en deze te koppelen aan Twents onderzoek.

Postdoc Position in Integrated HTA for Sustainability & Workforce Challenges

We are seeking a Postdoctoral Researcher with a strong quantitative background to work across two strategically connected research projects, addressing some of the most pressing challenges in contemporary Health Technology Assessment (HTA): labour scarcity in healthcare and sustainable healthcare decision-making.

Project 1: Sustainable Cardiac Care – A Forward-Looking HTA Framework
This project develops an integrated, future-oriented HTA framework to evaluate the sustainability of two clinically equivalent procedures for low-complexity multi-vessel coronary artery disease: coronary artery bypass grafting and percutaneous coronary intervention.

The project combines Multi-Criteria Decision Analysis (MCDA), Discrete-Event Simulation (DES), and Life Cycle Assessment (LCA) to evaluate clinical, economic, environmental, organizational, and patient-centred outcomes across full patient journeys. Results will inform national decision-making and provide a transferable framework for broader healthcare applications.

Project 2: Better cost estimates for scarce human resources in HTA using the production possibilities frontier
The shortage of qualified personnel already is an issue in Dutch healthcare, and this will only increase in the future. Hence, healthcare innovations need to be evaluated for their impact on use of scarce human resources. In principle, existing methods for cost-effectiveness analysis can do this, but the incorporated costs do not reflect labour scarcity due to labour market failures. In this project, we investigate methods for better estimation of labour costs, reflecting scarcity, by further developing methods from data envelopment analysis. We aim to develop a new method that can be applied in economic evaluations to enhance more efficient use of scarce resources, leading to lower waiting times and/or better care. We will apply the new methodology to two case studies, in mental health and pharmacy care.

Postdoc position in Numerical Analysis

We are looking for an enthusiastic and highly motivated postdoc candidate to join as soon as possible the Mathematics of Computational Science (MACS) group within the Department of Applied Mathematics at the University of Twente. You will work under the supervision of Dr. Gregor Gantner on a project dedicated to advancing the field of adaptive methods for time-dependent partial differential equations (PDEs). The position is funded by the Dutch Research Council (NWO) via the Vidi research project Optimal adaptive spacetime boundary and finite element methods.

The project focuses on the design, numerical analysis, and implementation of novel numerical algorithms to efficiently solve parabolic PDEs such as the heat equation. More precisely, you shall investigate space-time finite element methods (FEM) and space-time boundary element methods (BEM), which both discretize the PDE as a whole, treating time as yet another dimension. In particular, this allows for fully flexible local mesh refinement in the space-time cylinder. You will develop suitable a-posteriori computable estimators for the discretization error to adaptively steer the mesh refinement and mathematically prove that the resulting algorithms converge optimally with respect to the number of mesh elements. You will further implement and thereby validate the theoretical results through numerical experiments.

You will conduct your research in a collaborative, international, and interdisciplinary environment that values both academic excellence and open exchange of ideas. The MACS group offers a stimulating atmosphere with regular discussions, opportunities for collaboration, and access to expertise in numerical analysis, dynamical systems, and high-performance computing. The position includes funding to present your work at leading
conferences and engage in international collaborations.

PhD position in Numerical Analysis

We are looking for an enthusiastic and highly motivated PhD candidate to join as soon as possible the Mathematics of Computational Science (MACS) group within the Department of Applied Mathematics at the University of Twente. You will work under the supervision of Dr. Gregor Gantner on a project dedicated to advancing the field of adaptive methods for time-dependent partial differential equations (PDEs). The position is funded by the Dutch Research Council (NWO) via the Vidi research project Optimal adaptive space-time boundary and finite element methods.

The project focuses on the design, numerical analysis, and implementation of novel numerical algorithms to efficiently solve parabolic PDEs such as the heat equation. More precisely, you shall investigate space-time finite element methods (FEM) and space-time boundary element methods (BEM), which both discretize the PDE as a whole, treating time as yet another dimension. In particular, this allows for fully flexible local mesh refinement in the space-time cylinder. You will develop suitable a-posteriori computable estimators for the discretization error to adaptively steer the mesh refinement and mathematically prove that the resulting algorithms converge optimally with respect to the number of mesh elements. You will further implement and thereby validate the theoretical results through numerical experiments.

You will conduct your research in a collaborative, international, and interdisciplinary environment that values both academic excellence and open exchange of ideas. The MACS group offers a stimulating atmosphere with regular discussions, opportunities for collaboration, and access to expertise in numerical analysis, dynamical systems, and high-performance computing. The position includes funding to present your work at leading conferences and engage in international collaborations.

Software Developer edge computing and internet of things sensors

The University of Twente, Faculty ITC wishes to increase the number of women in the faculty to have a more balanced staff profile. During all phases of the selection process, we will therefore prioritize selecting women who fit the profile.New affordable in-situ sensors are part of the Internet of things (IoT) and enable us to build denser environmental sensor networks that measure phenomena more efficiently which vary greatly in space and time, such as air or noise pollution. However, such dense wireless sensor networks can put additional strain on bandwidth in areas with less network coverage, and they threaten citizens' privacy. One solution is to move the processing and analysis of collected geodata to the sensors. This type of edge computing has been made possible by advances in deep learning methods on embedded hardware, e.g., TensorFlow Lite for microcontrollers.

You will support our ambitions by working together with colleagues from several departments on projects that use small, affordable sensors (embedded devices) to collect environmental data and submit this data wirelessly. This position will therefore advance our understanding of processing geodata on the (network) edge and combine different geodata sources (remote and in-situ) in reproducible, distributed (networked) workflows. More specifically, you will advance the state-of-the-art in embedded devices and Internet of things technologies (hardware platforms, network protocols) by designing software solutions for environmental sensing using diverse open hardware platforms, adapt and implement major developments in the application of machine learning / AI on embedded devices, and propose and implement new workflows that incorporate IoT technology in research and capacity development projects.