NWO Take-off approvals for VU, UvA and Amsterdam UMC

As a result of the spring 2023 round of the NWO Take-off Grant, no less than seven Amsterdam projects, developed at Vrije Universiteit Amsterdam, Universiteit van Amsterdam and Amsterdam UMC, have been awarded a Take-off grant! Through the grant, academic and innovative starters can investigate whether their science-based innovative ideas are feasible and commercially applicable. Also, they can explore the possibility of starting a business based on knowledge innovations from knowledge institutions.

Next Take-off round fall 2023
The next Take-off round is open. Here, both feasibility studies and early-stage projects can be applied for. Please do always involve IXA in your application to maximise your success chance in time. Find out who to contact at: team of business developers.

Active scouting role by IXA
At IXA we consider the NWO Take-off grants, phase 1 and 2, as important valorisation instruments. Business Developers from IXA actively scout for new eligible projects throughout the year and provide researchers with support: assisting in the proposition development, formulating the business case, and filling out the application. Additionally, a helping hand with gaining support letters from outside parties is possible. Note that, for the Take Off Phase 1, IXA needs to write a letter of intent regarding the future licensing of the intellectual property.

The projects that will receive the NWO Take-off grant are:

Projects Vrije Universiteit Amsterdam

• International training centre for Psychological Interventions for Crisis-affected Populations (PICAP)

Dr. E.M. Sijbrandij – Common mental disorders (depression, anxiety and post-traumatic stress disorder) lead to individual suffering and high economic costs. People affected by war and crisis are more likely to experience these disorders, but have poor access to care. Vrije Universiteit (VU) Amsterdam has conducted research into the effects of “scalable”psychological  programs developed by the World Health Organization (WHO). These programs can be trained to lay counselors and are therefore much faster and more widely applicable, and cheaper than regular mental health care. We will develop a business and operational plan to start-up an international training academy hosted close to VU.

• DeepEye: Making eye-tracking scalable and accessible

Dr. A.V. Belopolsky – Eye-tracking is a powerful and widely used methodology because it can provide direct insights in human information processing. Being active users ourselves, we realized that eye-tracking research suffers from 1) not being easily scalable, since it involves hardware, and from 2) not being easily accessible, since it is not always possible to reach participants we need. To solve these problems we developed DeepEye which allows to run high quality eye-tracking online using a webcam. During Take-Off we will test hypotheses about consumer demand in potential markets, explore different ways to make our product available and conduct several technical benchmark tests.

• DBugIT: The Platform for an Exciting and Unbiased Software Testing Experience

Dr. ing. N. Silvis-Cividjian – Although paramount for a safe society, software testing is often perceived as a boring, unrewarding activity. Moreover, the assessment of those few who wish to enter the field, is not always fast, reliable and unbiased. DBugIT is an interactive online tool, recently developed at the Vrije Universiteit (VU) that solves these problems by engaging users in an exciting bug-hunting game, replacing the traditional job interviews and pen-and-paper exams. After receiving high appreciation inside VU, DBugIT is ready to grow and become the No.1 game for an exciting, unbiased software testing learning and recruiting experience.

• Energy trading: be ahead of the market by AI long-range weather forecasting

S.P. Vijverberg MSc – Reliable weather forecasts weeks to months ahead would be enormously valuable for many economic sectors, including energy, agriculture, and water management. At these longer timescales, existing operational weather predictions have little or no predictability. We developed a generic Artificial Intelligence-based forecast method and demonstrated that it greatly outperforms the current state-of-the-art in several regions including the United States and Europe. We combine innovative AI methods with expert climate knowledge to deliver reliable forecasts months ahead. We will develop targeted forecast products for the energy sector which is increasingly reliant on weather-dependent renewable energy production and affected by intensifying extreme weather.

Projects Universiteit van Amsterdam

• Classical-Quantum Hybrid Algorithms with Qudit Architectures

Dr. A. Safavi-Naini – In this project we will build a software which will help to solve the most challenging computational problems in industry, including scheduling, healthcare, finance or drug development, using cutting edge mathematical methods combined with the emerging quantum computers. We use the future generation of quantum bits, the d-dimensional qudits, to demonstrate the quantum advantage they provide in industrially relevant applications.

• A neurotech solution for sleep and memory improvement

Dr. L.M. Talamini – Need better sleep and want to learn while you’re sleeping? We are working on it! At the UvA, techniques and technology have been developed to improve sleep quality via neurostimulation. A wearable solution is being developed that can be taken out of the lab into academic, clinical or consumer markets. The wearable uses a patented, lightning-fast technology to analyse brain activity of sleepers. Based on this, sound pulses can be timed to amplify deep sleep waves or stimulate learning during sleep.

Projects Amsterdam UMC

• From the Workbench to the Benchmark: AI Learning Analytics for Next-Generation Surgical Teams

Prof. dr. M.P. Schijven – Adequate assessment and monitoring of skills of surgical residents is challenging. As a result, residents are at risk of -covertly- being under-skilled. With this grant, we aim to assess the feasibility of saturating a new Artificial Intelligence (AI) algorithm to evaluate surgical skills in workbench settings validly. Opportunities to improve can thus be identified objectively and reliably, without the need for a human observer to score performance. This helps safeguard residents from being inadequately prepared for the job, ultimately benefiting society by preventing loss of productivity in the surgical workforce.