Phd Position

vor 3 Monaten

Oldenburg, Deutschland Carl von Ossietzky Universität Oldenburg Vollzeit

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, Faculty VI forms the University Medicine Oldenburg. Furthermore, there is a close cooperation with the University Medicine of the University of Groningen.

The Division _AI4Health _lead by Prof. Dr. Nils Strodthoff aims to fill

one **PhD position (M/F/d) for 3 years**
(E 13 TV-L, 85 %)
**at earliest convenience**. The position is suitable for part-time employment.

About us:
**What we expect from you**:
We expect an outstanding university degree (MSc/Diploma) in mathematics, physics, computer science, electrical engineering or related fields of study. We expect the ability to work in a team, an analytical way of thinking, an independent working style and very good English language skills, both written and spoken. Technical prerequisites are a solid theoretical as well as applied knowledge in the area of machine learning (the latter documented through own projects), in particular in the domain of deep neural networks, as well as good programming skills in Python and proficiency in the machine learning framework Pytorch.

Optional requirements are applied knowledge in the processing of (physiological) time series data, uncertainty quantification as well as advanced knowledge in applied statistics.

**Your tasks**:
The position is part of the EU-project “QUMPHY”, a research project funded by European Metrology Programme for Innovation and Research (EMPIR) in cooperation with European metrology institutes and further university and non-university partners such as King’s College London and University of Cambridge. The project addresses uncertainty quantification for machine learning algorithms applied to photoplethysmography data. To this end, machine learning algorithms for different data representations are supposed to be compared for selected classification and regression tasks. Based on this, different approaches for uncertainty quantification are supposed to be adapted and implemented as well as appropriate metrics, which allow a quantitative comparison of the uncertainty information. Eventually, both components are supposed to be used for a comprehensive comparison in terms of both quantitative accuracy and uncertainty information. Project results are supposed to be deposited in public code repositories. The work for the advertised position will focus on the implementation/training of machine learning algorithms for photoplethysmography data as well as the adaptation and implementation of methods for uncertainty quantification as well as related documentation. The position allows working for a doctoral degree, which is also strongly encouraged.

**What we offer**:

- Payment according to collective bargaining agreement (annual special payment, company pension scheme, wealth-building benefits) including 30 days of annual leave.
- Support and guidance during the onboarding phase.
- A family-friendly environment with flexible working hours (flextime) and the possibility of partial remote work.
- Company health promotion benefits.

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