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.