Research Associate

About us

The School VI of Medicine and Health Sciences comprises the fields of human medicine, medical physics and acoustics, neurosciences, psychology and health services research. Together with the four regional hospitals, School VI forms the University Medicine Oldenburg. Furthermore, the university cooperates closely with the University Medicine of the University of Groningen.

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 a project funded by the German Research Foundation (DFG) titled "SELPHY-TS: Self-supervised Learning for PHYsiological Time Series," which focuses on learning representations from physiological time series and analyzing them. To this end, self-supervised learning techniques will be applied both to time series alone and to combinations of time series and metadata. The obtained representations will be examined using various methods, including those from the field of concept-based Explainable AI (XAI). The project involves collaborations with Charité Universitätsmedizin Berlin, UMCG Groningen, and the University of Cambridge.

Your tasks

• Conceptualization, adaptation, and implementation of methods in the field of self-supervised learning
• Fine-tuning of pre-trained models for various application scenarios
• Analysis of the latent representations of pre-trained models
• Publication in scientific journals and presentations at scientific conferences

Your profile

Required qualifications:

• Completed academic degree (Diploma [University]/Master's) in computer science, physics, mathematics, or related fields
• Comprehensive theoretical knowledge in machine learning, particularly deep learning, as well as extensive practical experience in training neural networks (demonstrated through personal projects)
• Strong proficiency in the Python programming language and the PyTorch machine learning framework (with specific examples of usage contexts)
• Excellent command of English, both written and spoken

Preferred qualifications:

• Prior experience in the field of self-supervised learning
• Previous experience training large deep learning models (e.g., in multi-GPU setups)
• Advanced knowledge of modern model architectures (e.g., Transformers, state space models, modern recurrent models)
• Prior experience in processing physiological time series data, such as ECG, PPG, or EEG

We Offer

• Payment in accordance with collective bargaining law (special annual payment, public service pension scheme, asset-related benefits) incl. 30 days annual leave
• Support and guidance during your onboarding phase
• A family-friendly environment with flexible working hours (flexitime) and the possibility of pro-rata mobile work
• Benefits from the university's health promotion programme
• An extensive and free further education programme as well as programmes geared toward the promotion of early career researchers (https://uol.de/en/school6/early-career)

Our standards

The University of Oldenburg is dedicated to increase the percentage of female employees in the field of science. Therefore, female candidates are strongly encouraged to apply. In accordance to § 21 Section 3 NHG, female candidates with equal qualifications will be preferentially considered. Applicants with disabilities will be given preference in case of equal qualification.

Further information

The division AIHealth is headed by Prof. Dr. Nils Strodthoff. 

There is the possibility of personal scientific qualification (doctorate).

The division AIHealth is headed by Prof. Dr. Nils Strodthoff. There is the possibility of personal scientific qualification (doctorate).

Contact:

For further information, please contact Prof. Dr. Nils Strodthoff (nils.strodthoff@uol.de).