Connectionists: [JOBS] Research Associate in the field Machine Learning and AI for Mechatronic Systems (SAIL subproject A.4: Care bed robotics)

[Wolfram Schenck]

POSITION: Research Associate in the field Machine Learning and AI for 
Mechatronic Systems (SAIL subproject A.4: Care bed robotics)
LOCATION: Bielefeld University of Applied Sciences, Bielefeld, Germany

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With more than 10,000 students, Bielefeld University of Applied Sciences 
is the largest university of applied sciences in East Westphalia-Lippe 
(OWL). Based in Bielefeld, Minden and Gütersloh, it has an excellent 
network not only in the OWL region, but also nationally and 
internationally through diverse contacts, partnerships and cooperations 
in science, economy, politics and culture. The Faculties of Design, 
Minden Campus, Engineering and Mathematics, Social Sciences, Business 
and Health strive for high quality in teaching and research.

Within the framework of the funding announcement “Networks 2021” 
(Netzwerke 2021) by the Ministry of Culture and Science of the State of 
North Rhine-Westphalia (MKW NRW) for the sponsored project “SustAInable 
Life-cycle of Intelligent Socio-Technical Systems” (SAIL), the Faculty 
of Engineering and Mathematics seeks to employ a

*** Research Associate in the field Machine Learning and AI for 
Mechatronic Systems (SAIL subproject A.4: Care bed robotics) ***

to start immediately. The full-time position is fixed-term until 31 July 
2026. Depending on individual qualification and tasks, remuneration will 
be up to salary group 13 TV-L (collective agreement of the federal 
state.) A cooperative doctorate can be pursued as part of the 
employment. Your workplace will be on Bielefeld Campus.

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Current systems that contain AI technology are primarily targeted at the 
introductory phase, a core component of which is training and 
customising AI models based on given sample data. The new SAIL research 
network is intended to develop the foundations for a sustainable design 
of AI components. The aim is to ensure that AI systems work 
transparently, safely and reliably throughout their entire product life 
cycle. In addition to Bielefeld University of Applied Sciences, this 
interdisciplinary network comprises Bielefeld University, Paderborn 
University and OWL University of Applied Sciences and Arts.

The project addresses fundamental research in the field of AI, its 
implications from the perspective of the humanities and social sciences, 
as well as concrete fields of application in Industry 4.0 and 
Intelligent Healthcare.

There are various life situations in which people depend on nursing care 
in a care bed during the course of or as a result of an illness. Care 
beds, especially intensive care beds with a large number of actuators, 
can be seen as robots themselves, taking care of the users in them, e.g. 
by carrying out repositioning movements to prevent pressure ulcers. In 
this subproject, an intensive care bed is available, which has RGB, 
depth image and infrared cameras as well as sensors for pressure 
distribution measurements on the bedding surface and whose drives are 
computer-controlled. The subproject begins with a fusion of the sensor 
data and uses ML-based modelling approaches to determine/estimate 
non-directly measurable process variables and patient characteristics 
from a subset of the sensors (e.g. estimation of the lying pressure 
distribution without pressure distribution measurement as a basis for 
repositioning movements of the bed). The AI questions that follow 
concern, for example, model individualisation, natural modes of 
interaction and adaptivity.

Tasks and responsibilities:
- Independent project coordination and implementation within the 
research network
- Conduct of independent research activities within the framework of the 
research project:
  ➜ Identification and definition of requirements for the workflow, the 
algorithms to be used and the underlying mechatronic infrastructure
  ➜ Design and conduct of measurement campaigns with test subjects
  ➜ Research into hybrid models as well as AI and ML methods for robust 
and interpretable prediction of process variables and patient 
characteristics/body parameters
  ➜ Research into approaches to the continuous adaptation of hybrid models
  ➜ Implementation of ML approaches for controlling the intensive care 
bed demonstrator
  ➜ Implementation of the methods on the demonstrator
  ➜ Generalisation of the developed approaches
  ➜ Application and transfer of research results
- Instruction and guidance of student and research assistants
- Knowledge transfer/publications/conference presentations
- Support of project lead in other project activities within the scope 
of SAIL
- Implementation of creative ideas in the research network as well as 
interdisciplinary work

You will perform these tasks independently in coordination with 
Professor Dr. rer. nat. Axel Schneider. In addition, you will work in an 
interdisciplinary team consisting of the research associates of the 
other universities and other professors.

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Requirements:
- Master of Science/Engineering from a university or university of 
applied sciences in electrical engineering, computer science, 
biomechatronics or related subjects
- In-depth expertise in the collection, processing and algorithmic 
evaluation of (image) sensor data
- Expertise in machine learning, embedded and robotic systems
- First practical experience with the methods and toolboxes of machine 
learning
- Very good programming skills (Python, C(++), MATLAB/Simulink, etc.)
- Excellent conceptual and analytical thinking and action
- Independent and autonomous way of working
- Outstanding intellectual capacity
- Experience in writing academic texts and presenting scientific work 
results
- Very good knowledge of written and oral German and English
- Excellent team spirit and communication skills, confident demeanour

Candidates are further required to make sure that their fixed-term 
employment does not exceed the limits prescribed by the 
Wissenschaftszeitvertragsgesetz due to previous employments.
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The ideal candidate should also bring:
- Experience in project coordination
- Experience in research activities, in the preparation of scientific 
reports and publications or activities as a student or research assistant
- Experience in handling hardware components (e.g. installation and 
integration of sensors)
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Benefits:
- Mobile work possible
- Opportunities for participation in qualification programmes
- Support offers for publications and patents
- University daycare facility “EffHa” and holiday care for 
schoolchildren on Bielefeld Campus
- 6 faculties with diverse partnerships and research collaborations in 
one of the most economically powerful regions in Germany
- Good accessibility by public transport
- Participation in the university sports programme from Bielefeld University

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Bielefeld University of Applied Sciences has received multiple awards 
for its successes in promoting equal opportunities and has been 
certified as a family-friendly university. Therefore, women are 
particularly welcome to apply, especially in the field of research and 
in technology, IT and crafts.
Applications from women will be given preference in case of equal 
suitability, skills and professional performance, unless reasons 
concerning the person of another applicant predominate.

Persons with severe disabilities are encouraged to apply, too. Subject 
to other applicable laws, severely disabled applicants with equivalent 
qualifications will be given preferential consideration.

Please find more detailed information on the SAIL subprojects at: 
https://jaii.eu/sail. Multiple applications for other subprojects in the 
network are expressly welcome. In this case, please include the 
reference numbers of the respective subprojects in your application.

If you have any questions relating to the content of the position we 
offer, please contact Prof. Dr. rer. nat. Axel Schneider via e-mail at 
axel.schneider[AT]fh-bielefeld.de.

** Are you interested? Please apply online only by 6 October 2022, 
stating the reference number 03231. **

Application link: 
https://app.fh-bielefeld.de/qisserver/rds?state=change&type=3&nextdir=sva/bwmsas&subdir=sva/bwm&moduleParameter=bwmSearchResult&next=TableSelect.vm&P_start=0&P_anzahl=100&navigationPosition=qissvaCareer%2Csvabwmstellenuebersicht&breadcrumb=sva_bwm_stellenuebersicht&topitem=qissvaCareer&subitem=svabwmstellenuebersicht


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