CIR/03/2023: Medical Research Assistant (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 part-time (8 hrs./week) Medical Research Assistant is available at the Computational Imaging Research (CIR) Lab, led by Prof. Dr. Shadi Albarqouni, in the Clinic for Diagnostic and Interventional Radiology of the University Hospital Bonn, University of Bonn:
Medical Research Assistant (m/f/d)
starting July 2023 or as agreed upon. The position is initially limited to two years, with the possibility of extension.
The medical research assistant 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. The medical research assistant is the key component in developing the next generation of AI in Medicine by providing high-quality annotations for medical imaging and radiology/pathology reports.
- Assisting the team members in the data collection process
- Annotating the collected imaging data for various projects using readily available tools
- Performing quality control of annotated images collected by different partners
- Collaboration with team members and clinical partners must be maintained
- Medical Doctorate (MD) degree
- Passionate about Machine/Deep Learning in Medical Imaging
- 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
What we offer you:
- A secure future: remuneration according to the German salary scale TV-Ä (Ä1/Ä2 – 20%)
- 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.
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/03/2023 in your email’s subject to Prof. Dr. Shadi Albarqouni.
- MA Thesis: Deep Learning based model for detection and grading of prostate cancer using mpMRI and MR-Fingerprinting
- MA Thesis: Deep Learning-based method for virtual ECV in cardiac magnetic resonance imaging
- Course: Introduction to Machine Learning
- CIR/05/2023: Postdoc position in Computational Medical Imaging (m/f/d)
- Cathnets: detection and single-view depth prediction of catheter electrodes