CIR/01/2024: Ph.D. position in AI in Medicine with Large Language Models (m/f/d)

We combine excellence in research, teaching, and patient care. The University Hospital Bonn (UKB) is a maximum care hospital with more than 1,300 beds. With around 38 clinics and 31 institutes as well as more than 8,000 employees (over 5,000 full-time staff), the UKB is one of the largest employers in Bonn. Every year, the UKB treats around 50,000 inpatients and around 35,000 emergencies, as well as provides over 350,000 outpatient treatments.

The following (TVL E13 - 75%) Ph.D. position is available at the Computational Imaging Research (CIR) Lab, headed by Prof. Dr. Shadi Albarqouni, in the Clinic for Diagnostic and Interventional Radiology of the University Hospital Bonn, University of Bonn:

Ph.D. position in AI in Medicine with Large Language Models (m/f/d)

starting Dec 2024 or as agreed upon. The position is initially limited to three years, with the possibility of extension.

The Ph.D. position will be based in the newly founded research lab for Computational Imaging Research (CIR), which aims to develop i) fully automated, highly accurate innovative computational methods that save expert labor and efforts, and mitigate the challenges in medical imaging; namely the availability of a few annotated data, low inter-/intra-observers agreement, inter-/intra-scanners variability and domain shift, ii) innovative deep Federated Learning algorithms that can fairly distill and share the knowledge among AI agents in a robust and privacy-preserved way, and iii) affordable AI algorithms suitable for low-quality data generated by low-resource settings and point-of-care devices. As AI technology becomes the de facto knowledge discovery approach in many industries, including Healthcare, robustness, and explainability have emerged as key factors to be considered in future EU regulations.

In this project, the Ph.D. candidate will focus on integrating Large Language Models (LLMs) into deep learning frameworks for medical imaging. LLMs offer unprecedented capabilities in processing and interpreting unstructured text data, which can be leveraged to enhance image analysis, annotation, and the generation of synthetic data for training models. The candidate will explore the application of LLMs to improve the accuracy and robustness of medical imaging models, particularly in scenarios with limited annotated data or when dealing with diverse and complex clinical environments.

The Ph.D. candidate will have the opportunity to work on cutting-edge research that bridges the gap between advanced natural language processing techniques and medical image analysis. This includes collaborating with clinicians to identify clinical use cases where LLMs can contribute to improved diagnostic workflows, as well as developing methods to ensure the transparency, interpretability, and regulatory compliance of AI models incorporating LLMs in healthcare.

The Ph.D. candidate will be enrolled in the the Faculty of Mathematics and Natural Science at the University of Bonn. If you have a strong background in deep learning, medical imaging, and an interest in exploring the potential of LLMs within this domain, this position offers a unique opportunity to be at the forefront of AI research in medicine.

Your responsibilities:

• Build and create a few clinical use cases for benchmarking existing state-of-the-art (SOTA) algorithms. This includes running baselines and pre-/post-processing pipelines
• Develop innovative computational algorithms for Robustness against data and model poisoning attacks, and Explainability of Deep Learning models
• Publish and present scientific outcomes at Intl. conferences and high-impact journals
• Maintain close collaboration with the team members and clinical partners

Your qualifications:

• M.Sc. in Computer Science, Machine Learning, or equivalent with an interest in Medical Imaging
• Strong knowledge of Machine/Deep Learning with experience in discriminative models, adversarial attacks, and Bayesian neural networks
• Excellent programming skills in Python and PyTorch including fundamental software engineering principles and machine learning design patterns
• Excellent analytical, technical, and problem-solving skills
• Be highly motivated and a team player with excellent communication and presentation skills, including experience in communicating across discipline boundaries
• Fluent command of the English language

Desirable qualifications:

• Track record of publications at top-tier conferences and high-impact journals in the field, e.g., CVPR, MICCAI, MIDL, IEEE TMI and MedIA.
• Hands-on experience with the MONAI framework
• Working in a Linux environment, with experience in shell and cluster (SLURM) scripting
• Fluency in spoken and written German

What we offer you:

• A secure future: remuneration according to the German salary scale TV-L (E13 - 75%)
• Flexible for families: flexible working time, home office, onsite nursery, and parental care.
• Provisions for later: company pension scheme
• Discounted public transport ticket: discounted ticket for public transport (VRS) on-site health management service: Numerous health promotion offers
• Employer benefits: Discounted offers for employees
• Subsidized continuing education and training

The University of Bonn is committed to diversity and equal opportunity and is certified as a family-friendly university. It aims to increase the proportion of women in areas where women are under-represented and to promote their careers in particular. Therefore, we strongly encourage applications from qualified women. Applications will be handled in accordance with the State Equality Act (Landesgleichstellungsgesetz). Applications from individuals with a certified severe disability and from those of equal status are particularly welcome.

Contact:

If you meet the requirements and you are looking for a challenging job? Do not hesitate and send your application including a cover letter (highlighting your qualifications), a detailed CV (with links to previous projects and code), scanned academic degrees, and the contact details of two referees (preferably by e-mail in a single PDF file up to 5 MB in size), quoting the job advertisement no. CIR/01/2024 in your email’s subject to Prof. Dr. Shadi Albarqouni.