3y PhD position: Machine Learning for photoplethysmography data

University of Oldenburg


3 Jul 2023
Job Information

Organisation/Company
University of Oldenburg
Department
School of Medicine and Health Services
Research Field
Computer science » Other
Researcher Profile
First Stage Researcher (R1)
Country
Germany
Application Deadline
31 Jul 2023 – 23:59 (Europe/Berlin)
Type of Contract
Temporary
Job Status
Full-time
Hours Per Week
38,5
Offer Starting Date
1 Sep 2023
Is the job funded through the EU Research Framework Programme?
H2020
Reference Number
EMPIR 22 HLT 01 – QUMPHY
Is the Job related to staff position within a Research Infrastructure?
No

Offer Description

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:

In the division AI4Health, we investigate methodological questions in the domains of self-supervised/label-efficient learning as well as explainability of deep neural networks (XAI) with particular focus on applications in the biomedical domain. Further information on the division is available at https://uol.de/en/ai4health .

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.

Requirements

Research Field
Computer science » Modelling tools
Education Level
Master Degree or equivalent

Skills/Qualifications

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.

 

Specific Requirements

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.

Languages
ENGLISH
Level
Excellent

Research Field
Computer science
Years of Research Experience
1 – 4

Additional Information
Benefits

  • 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.
  • Extensive free further education program as well as dedicated support for young scientists (https://uol.de/en/school6/early-career ).

Eligibility criteria

The University of Oldenburg is dedicated to increasing the percentage of female employees in the field of science. Therefore, female candidates are strongly encouraged to apply. In accordance with Lower Saxony regulations (§ 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.

Selection process

Specific questions on this position can be addressed to Prof. Dr. Nils Strodthoff ([email protected] ). Please send your application documents (including CV, letter of motivation, list of publications, university and high school certificates) via email to [email protected] (max. 2 pdf files up to 10MB, subject: QUMPHY). All applications received until the 31st of July 2023 will be considered.

We stress that application and interview costs cannot be covered. We do not cooperate with application portals, e.g. Indeed. Please send your application to the address mentioned above.

Additional comments

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

Website for additional job details
https://uol.de/stellen?stelle=69676

Work Location(s)

Number of offers available
1
Company/Institute
Oldenburg University
Country
Germany
City
Oldenburg
Postal Code
26129
Street
Ammerländer Heerstr. 140
Geofield

Where to apply

E-mail
[email protected]

Contact

City
Oldenburg
Website
https://uol.de/en/ai4health
Street
Ammerländer Heerstr. 114-118
Postal Code
26129

STATUS: EXPIRED

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