Research Associate

Faculty VI Medicine and Health Sciences encompasses the areas of human medicine, medical physics and acoustics, neuroscience, psychology, and health services research. Together with the four regional hospitals, Faculty VI forms the University Medicine Oldenburg. Furthermore, there is close collaboration with the University Medical Center of the University of Groningen.

The division "AI4Health" (headed by Prof. Dr. Nils Strodthoff) in the Department of Health Services Research of Faculty VI - Medicine and Health Sciences at the University of Oldenburg are seeking to fill a position at the earliest possible date for a

Research Associate (m/f/d)
(Salary group 13 TV-L)

with 65% of the regular weekly working hours (currently 39.8 hours). The position is limited to 36 months. There is an opportunity for further academic qualification (doctorate).

In the AI4Health division, methodological questions in the areas of self-supervised/label-efficient learning and explainability of deep neural networks (XAI) are being developed, particularly for use in biomedical applications. Further information about the department can be found at https://uol.de/en/ai4health.

The position is part of the intramurally funded profile initiative "University Diagnostics Center", which aims to enable the diagnostic disciplines (University Institute for Clinical Chemistry and Laboratory Medicine, University Institute for Medical Genetics, University Institute for Medical Microbiology and Virology, University Institute for Diagnostic and Interventional Radiology, and the Division of Immunology) of the University Medicine Oldenburg to perform analyses using machine learning methods and to establish multimodal prediction models in research, teaching, and patient care in the long term.

Tasks

The advertised position aims to develop data-driven prediction algorithms in the context of various diagnostic disciplines. In a first step, prediction models for single modalities are to be developed for selected use cases. In a second step, different modalities are to be combined to demonstrate the feasibility of multimodal predictions. The focus of the advertised position is on data from laboratory medicine, immunology, and microbiology. The project has a strong interdisciplinary character and requires, in addition to prior knowledge in the field of machine learning, an enjoyment of interdisciplinary collaboration with experts from different diagnostic application areas, learning about different data modalities, and selecting and adapting suitable learning algorithms. Research results of both methodological and applied nature should be published in professional journals and presented at relevant conferences.

Your Profile: 

Requirements

• Completed university degree in Computer Science, Mathematics, Physics, or related fields
• Excellent command of English, both written and spoken
• Theoretical and practical knowledge (the former demonstrated through relevant courses/completed online courses, the latter demonstrated through personal projects) in the field of machine learning, especially in the area of deep neural networks
• Very good programming skills in Python and in the Machine Learning framework PyTorch
• High degree of independence, flexibility, and teamwork skills as well as willingness to work in an interdisciplinary manner

Desirable

• Experience in medical-diagnostic application fields, especially bioinformatics, demonstrated through completed courses or personal projects
• Proven experience in interdisciplinary communication
• Experience with scientific presentations and publications
• Knowledge of German

We offer:

• Payment according to tariff regulations (annual bonus, company pension scheme, capital-forming benefits) including 30 days of annual leave
• Support and guidance during your induction phase
• A family-friendly environment with flexible working hours (flextime) and the possibility of partial remote work
• Occupational health promotion services
• A comprehensive free training program as well as our own support program for young scientists (https://uol.de/medizin/nachwuchs)

The University of Oldenburg aims to increase the proportion of women in the scientific field. Therefore, women are strongly encouraged to apply. According to § 21 Para. 3 NHG (Lower Saxony Higher Education Act), female applicants with equal qualifications should be given preferential consideration. Applicants with disabilities will be given preference in case of equal qualification.

You don't know Oldenburg yet? Feel free to gather some first impressions through the following link: https://www.moin-in-oldenburg.de

Contact:

If you are interested or have questions, please contact Prof. Dr. Nils Strodthoff (nils.strodthoff@uni-oldenburg.de).