The Dutch Research Council (NWO) has awarded fourteen young TU Delft researchers from the Science (ENW) and Applied and Engineering Sciences (TTW) domains, a Veni grant of up to 320,000 euro. This will allow the laureates to further develop their own research ideas over the next three years. A total of 174 Veni grants were awarded.
In the ENW domain, seven Veni’s were awarded to:
Photo-active hydrogen bonded pairs for efficient artificial leaves
Dr. Tessel Bouwens, Applied Sciences (AS)
Using sunlight to generate the building blocks for medicines? Yes, we can, if we manage to solve the efficiency problem in artificial photosynthetic devices. This research proposes to address the efficiency problem by developing a molecular shuttlebus for delivering charged species at the desired location at the right time to circumvent that these reactive charged species will undergo destructive side reactions.
Using the concept of the molecular shuttlebus, I want to develop a new method to generate the building
blocks for medicines employing solar energy, without using the polluting fossil fuels.
Ray to Release: X-rays trigger cleavage of cytotoxins for synergistic radio-chemotherapy
Dr. Mark de Geus, Applied Sciences (AS)
Cancer therapy causes side effects to the patient which limits further treatment options. Antibody-drug conjugates (ADCs) and radiotherapy (X-rays) both cause localized damage to tumor cells to reduce side effects. This research project will develop new chemistry to combine these approaches. The antibody conjugate accumulates in tumor cells. Afterwards, X-rays act as molecular -scissorsto separate the antibody and the drug, allowing the drug to kill the tumor. The linker that connects the antibody to the
drug is chemically engineered to make it more sensitive to X-rays, which means that a lower dose of
radiation can be used
Personal page
Combinatorial applications in parameterized algorithms
Dr. Carla Groenland, Electrical Engineering, Mathematics & Computer Science (EEMCS)
Network algorithms are ubiquitous: you use them for example when finding the shortest route home. In this project, insights from combinatorics are used to find better algorithms, bringing fundamental mathematics a step in the direction of real-life applications. The project includes reconstructing properties from as few questions as possible (’’query reconstruction’’) and recognizing redundant steps in sorting algorithms (’’slow sorting’’). The latter has implications for routing algorithms.
More information
Adaptive Algorithms for Non-Stationary Reinforcement Learning
Dr. Julia Olkhovskaia, Electrical Engineering, Mathematics & Computer Science (EEMCS)
Reinforcement Learning (RL) is revolutionizing automation tasks such as autonomous driving and managing smart power grids. RL stands out for its unique ability to actively learn from interactions and adapt to new data. However, in rapidly changing environments, it is struggling to perform in large-scale problem settings. My research aims to overcome this by developing advanced RL algorithms that are not only adaptive but can also process a vast amount of environmental data in real time. This approach is
bridging the gap between theoretical RL models and their practical, real-world applications
More information
Characterization of organic matter through spectro-polarimetry
Dr. Sandra Potin, Aerospace Engineering (AE)
The light holds information on the surface it has been reflected on. This is currently used as a technique to detect signs of organic molecules, the building blocks of life in the Solar System. But this can be limited by the detection capacity of the space scientific instrument. This project proposes to use an intrinsic property of the light, polarization, to isolate and better identify the carbon-based molecules.
More information
Advancing Subsurface Characterization via Ensemble Nonlinear Data assimilation (ASCEND)
Dr. Max Ramgraber, Civil Engineering and Geosciences (CEG)
The subsurface is important for water supply and the energy transition, yet difficult to access and observe directly. Limited information entails uncertainty, which can be resolved with data assimilation (DA). Currently, most DA methods are either simplistic or computationally prohibitive. This project develops a scalable DA algorithm that can be tailored to the system’s demands, which permits statistical analyses that are as simple as possible and as complex as necessary. These analyses are instrumental for efficient and rigorous engineering in the subsurface.
More information
Ultrafast detection of short-lived intermediates during the electrocatalytic CO2 reduction reaction
Dr. Yan Vogel, Applied Sciences (AS)
The petrochemical industry provides us with the products required to sustain our current living standards, but also releases ~40 Gt a year of carbon dioxide driving climate change. The use of renewable electricity to convert carbon dioxide into valuable chemicals can solve this problem.
However, we are currently unable to obtain the desired products because of the lack of understanding of the carbon dioxide reactions. This project aims to unlock the mechanism behind these reactions by using advanced spectroscopic tools, leading to a new method of chemical visualization for the production of clean chemicals.
More information
RECLIMATE: Resource-efficient climate-resilient buildings by multi-hazard risk modelling and
resilience-oriented decision-making
Dr. Simona Bianchi, Architecture and the Built Environment
Climate change poses higher risks to our vulnerable homes. Urgent adaptation solutions are needed to create resilient urban communities. Yet, current design and assessment methods do not consider multi- hazard resilience quantifications and extreme heat consequences, which results in ineffective solutions.
This project pioneers a comprehensive assessment framework to measure the heat vulnerability of buildings and quantify their overall resilience in the face of climate uncertainties. It introduces novel multi-disciplinary design methods, frameworks and digital tools for resource-efficient resilient designs and retrofits in the era of climate-induced extremes.
More information
The NEXT WAVE: enabling indefinite wave equation simulations for key-enabling technologies
Mr. Dr. ir. Vandana Dwarka, Electrical Engineering, Mathematics & Computer Science (EEMCS)
Modern scientific innovations use simulations to push beyond limits. Plasma confinement and quantum mechanics rely on simulations of embedded wave equations. Unfortunately, one simulation can take months to complete. Simulation hurdles stem from a complex mathematical feature of these wave equations, which prevents developing theory to support robust simulation algorithms. Hence, algorithms lag years behind advances in synergistic modelling of coupled dynamics to mimic real-life systems. This research eliminates that backlog by combining novel theory and co-design with industry
experts to enable and accelerate simulations of key-enabling technologies, such as fusion reactors and
quantum computers
More information
Energy-Efficient Real-Time Edge Intelligence for Wearable Healthcare Devices
Dr. Chang Gao, Electrical Engineering, Mathematics & Computer Science (EEMCS)
In this innovative project, researchers are developing new software and hardware technology to make healthcare wearables, like eye movement trackers, hearing aids, and heart rate monitors, smarter and more efficient. By processing personal data and artificial intelligence algorithms for healthcare directly wearable healthcare and enhance privacy. This approach also reduces energy use, promising longer battery life and more sustainable devices. The technology could transform how we monitor health conditions, making it quicker, more secure, and accessible to a wider audience.
More information
Intensifying electrochemistry through downscaling - Micro ElectroChemical Systems (MECS)
Dr. Adrian Mularczyk, Applied Sciences (AS)
Smaller size at larger scale. Producing complex reactor geometries, smaller than a human hair, with precision, reliability and speed has been the key to many technological advances. Achieving this in the field of electrochemical systems is not trivial and requires a symbiotic interplay between several disciplines from engineering and chemistry. If successful, this can unlock a revolution in our way of designing and producing electrochemical reactors and boost their capabilities to be integrated in our energy grids and chemical conversion infrastructure.
Personal page
Revealing Hidden Networks of Coastal Sediment Pathways via Laboratory & Numerical Experiments Dr. ir. Stuart Pearson, Civil Engineering and Geosciences (CEG)
Sediment is essential for creating a safe and sustainable coast. In this project, I will track the pathways that sand takes on an experimental laboratory beach and extend those findings with computer models.
By revealing the interconnected network of sediment pathways shaping our coast, we can better understand how to manage the sediment that builds ecosystems and protects us against flooding.
More information
Fluidic Sensing: Giving Soft Robots the Sense of Touch
Dr. Shibo Zou, Mechanical Engineering (ME)
Soft robotics holds the promise to handle delicate objects with human-like dexterity and care in real- world environments, from greenhouses to operating theatres. Sensory feedback is crucial to achieve high autonomy. Fluidic sensing extracts feedback mechanically through the actuation pressure variation induced by shape change and can potentially bring the sense of touch to soft fluidic robots. However, this actuation pressure variation is currently too low for practical applications. I aim to unravel the underlying mechanical design principles that can amplify the actuation pressure variation in fluidic sensing and translate these principles into multifunctional soft robotic devices for real-world applications.
-With this Veni, I will uncover the mechanical design principles to enhance the self-sensing performance of soft fluidic actuators. The design knowledge has the potential to advance the readiness of soft robots for industrial automation."
More information
Signal Processing and Learning from Higher-Order Network Dynamics
Dr. Elvin Isufi, Electrical Engineering, Mathematics & Computer Science (EEMCS)
Networks, such as those that distribute water to our homes or our brain, generate streams of data according to their topology but conventional processing techniques do not fully capture their complex dependencies. The research will investigate novel techniques to better leverage the network structure for processing these data so as to detect more accurately brain anomalies, forecast future water demands, and make them deployable to a large-scale setting.
Talent programme
The NWO Talent Programme gives researchers the freedom to pursue their own research based on creativity and passion. The programme encourages innovation and curiosity. Curiosity-driven research contributes to and prepares us for tomorrow’s society. That is why NWO focuses on a diversity in terms of researchers, domains, and backgrounds. Together with the Vidi and Vici grants, Veni is part of the Talent Programme.
NWO selects researchers based on the academic quality and innovative character of the research
proposal, scientific and/or societal impact of the proposed project and the quality of the researcher.
Click here for answers to frequently asked questions about the Talent programme.
Please read the.