“Postdoc/researcher probabilistic spiking neural networks (m/f/x)

We are looking for a motivated postdoctoral researcher with expertise in the area of spiking neural networks and an interest in (applications of) probabilistic computing.

The postdoc candidate will participate in the NWO NWA project "Acting under uncertainty". The goal of this project is to advance our understanding of how humans are able to act in complex and uncertain circumstances, and how such knowledge can be applied to smooth our interaction with emerging autonomous vehicles. The whole project encompasses a broad spectrum of academic, industrial and societal partners and includes a large group or researchers. The postdoc position will advance state-of-the-art in spiking neural networks and their real-world applicability in applications like drones and other autonomous vehicles, in close collaboration with labs at TU Delft, IMEC NL, and NLR. Some project management related tasks will also be part of the candidate's responsibilities.

2 PhD-studenten in Neuro-AI of de Ontwikkeling van Visie (m/v/x)

The Curriculum of Sight - How Visual Processing Develops in Brains and Machines.

For animals, it is imperative to learn as quickly as possible. To do this, we know that the mammalian visual system utilizes "inductive biases"—inherent assumptions that streamline learning for specific environments. In this project, we aim to investigate how rapid learning in mammals is facilitated by the learning curriculum shaped in the developmental stages of human infants. By comprehensively measuring and documenting these developmental stages and the associated inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics its processing capabilities but also its adaptability, leveraging early developmental data and ecological validity. Infant data will be acquired in collaboration with Dr. Tessa Dekker (University College London) and Dr. Ingmar Visser at UvA. The project thus aims to quantify and model the remarkable learning efficiency of the human visual system.

The project is an interdisciplinary collaboration between the the Machine Learning group at CWI in Amsterdam (Prof.dr Sander Bohte) and the Brain & Cognition group (Dr. Steven Scholte) at the UvA, where the PhD at UvA will focus more on quantifiying and modeling developmental stages, and the PhD at CWI on developing detailed neural models.

PhD student (m/f/x) on the subject of Fast and low-dose medical imaging

The project is part of the IMAGINE open innovation lab (https://research.umcutrecht.nl/news/imagine-takes-off/), and aims to develop novel computational methods for faster medical imaging with reduced radiation dose. The focus will be on (cone beam) CT and its use in image-guided interventions in oncology. One of the main challenges is to deal with high noise levels (due to the low radiation dose). One possible way to address this is to incorporate information gained from previous scans (e.g., MRI) of the same patient.

As a PhD student on this project, you will:

• Design and conduct research on computational methods for CT reconstruction in the context of image-guided interventions, focusing on improving quality of cancer treatment for patients as well as reducing workload for clinicians. Collaborate with experts in AI, data science, and medical imaging to develop integrated solutions
• Engage in translational research, bridging the gap between technological innovation and clinical application
• Contribute to publications and presentations to disseminate findings within the scientific community
• Participate in interdisciplinary meetings and workshops to foster knowledge exchange and innovation

Your work is vital to advancing less invasive treatment options, reducing patient recovery times, and optimizing healthcare resources. Your day-to-day research will take place in the computational imaging group at CWI, where you will have a chance to interact and collaborate with fellow PhD-students and research staff with a background in mathematics, computer science, and imaging science. It is expected that you will frequently interact with the other members of the IMAGINE open innovation lab (situated in Utrecht), as well as contribute to teaching at the Mathematical Institute at Utrecht University.

PhD position in Fundamental Techniques in Table Representation Learning

Goal of the Table Representation Learning (TRL) Lab

Approximately 120 zettabytes of data has been collected worldwide but less than 1% is actually used. Structured data as found, for example, in tables, spreadsheets, and relational databases, is prevailing in organizations and typically informs important decisions in governments and humanitarian organizations, healthcare and finance. Yet, while AI has demonstrated a high impact on applications on text and images, proportional progress on tabular data is lacking. With the TRL Lab (Table Representation Learning Lab), we aim to close this gap, by developing AI models and tools for tabular data, to help organizations, of any size, domain, and level of data literacy, get insights from structured data, efficiently, accurately and securely.

Goal of this PhD project
High-capacity neural models, such as transformers, have been pivotal for establishing general-purpose models for a wide variety of natural language tasks. Despite successful adaptations for structured data, our research has identified shortcomings for fundamental properties of tabular data. This research position will focus on exploring fundamental techniques for tabular-native models. This can involve, for example, studying new TRL model architectures, serialization and tokenization techniques, among others. A strong interest and background in AI and/or NLP are desired.

What you will be doing

• Inform a research agenda on the PhD topic for a timespan of four years.
• Develop fundamental AI techniques, new TRL models, and systems specific for tabular data.
• Publish reusable software and data artifacts where relevant.
• Communicate research outcomes through papers and talks at conferences, workshops, and beyond.
• Actively collaborate with other researchers in the TRL Lab (students, 4-5 PhDs, postdocs, PI) and external collaborators (e.g. University of Amsterdam, the UN, and Amsterdam UMC).
• Assist in relevant teaching activities at universities, such as thesis supervision and assisting in courses.

Postdoc (m/f/x) to develop computational models for electric breakdown of gases

Electric gas discharges occur in nature, most prominently in air in the form of lightning and its less visible precursors. Similar gas discharges occur in high-voltage equipment, such as the switchgear used in electric grids. Our transition to sustainable electric energy requires major extensions of this grid. However, switchgear and other high-voltage equipment currently often operate with SF6 gas, which is the worst greenhouse gas known. Within the project, we investigate electric discharges in “green” alternatives to SF6. For further information, see https://homepages.cwi.nl/~ebert/2023-ERCIM-News.pdf

We are seeking a postdoc to develop computational models of electrical breakdown of gases. Breakdown occurs in several stages: streamer discharges form, some of them can turn into leaders due to gas heating, eventually resulting in a spark. Simulating all these stages in detail is computationally unfeasible, so the goal is to develop a simplified model focusing on the leader stage. You will:

• Analyze experimental data and microscopic simulations
• Identify relevant physical features and parameters
• Apply machine learning techniques to build predictive models
• Collaborate closely with experimentalists and modelling experts

Project Environment
This position is part of a collaborative research project involving:

• Two PhD students at TU Eindhoven, conducting experiments
• A PhD student at CWI, focused on modelling streamer discharges
• A committee of industrial users, ranging from start-ups to multinationals, providing feedback several times per year

Our modelling group at CWI Amsterdam has extensive expertise in modelling the dynamics of gas discharges. We have developed several microscopic models for the early stages of electric discharges as well as analytical approximations to grasp their multiple spatial and temporal scales. Experimental partners at TU Eindhoven include the group of Sander Nijdam, an expert in measuring streamer discharges with highest resolution, and the group of Tom Huiskamp, who focusses on new solid state high voltage sources and their applications.