Connectionists: [JOBS] Research Associate in the field Machine Learning and AI (SAIL subproject R2.1: Self-aware AI, resilience and preparedness)

[Wolfram Schenck]

POSITION: Research Associate in the field Machine Learning and AI 
(SAIL-Teilprojekt R2.1: Self-aware AI, resilience and preparedness)
LOCATION: Bielefeld University of Applied Sciences, Bielefeld, Germany

-------------------------------------------------------------------------------

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 (SAIL 
subproject R2.1: Self-aware AI, resilience and preparedness) ***

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.

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.

The predictions of AI systems are fraught with uncertainty, often making 
their practical use difficult. There are several ways to tackle this 
uncertainty, e.g., anticipating and correcting invalid predictions or 
downstream strategies for dealing with invalid results. In this 
subproject, approaches from both directions are examined, considering 
both the algorithmic level and the user perspective. In this way, AI 
systems could identify false predictions in interaction with the user 
and improve accordingly. The developed methods are validated using 
experimental setups in the application areas “Intelligent Industrial 
Work Spaces” and “Adaptive Healthcare Assistance Systems.”

-----------------------------------------------------------------------------------------------------------------

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 AI workflow, 
the algorithms to be used and the underlying infrastructure
  ➜ Realisation of new ML approaches based on, e.g., techniques of 
active learning and modelling of uncertainty in prediction
  ➜ Development of user interfaces for the developed AI systems
  ➜ Generalisation and verification of the modular solution components 
on Bielefeld UAS’s own research infrastructure
  ➜ Conduct of studies with users of the newly developed systems
  ➜ 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.-Ing. Wolfram Schenck. In addition, you will work in an 
interdisciplinary team consisting of the research associates of the 
other universities and other professors.

----------------------------------------------------------------------------------------------------------------

Requirements:
- Master of Science from a university or university of applied sciences 
in one of the following fields: computer science, data science, 
electrical engineering, (bio-)mechatronics or cognitive sciences with 
specialisation in data-based modelling, statistics or machine learning
- In-depth expertise in data collection and analysis, machine learning, 
statistics, data mining and optimisation
- First practical experience with the methods and toolboxes of machine 
learning
- Very good programming skills (Python, R, 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 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.

----------------------------------------------------------------------------------------------------------------

The ideal candidate should also bring:
- Basic knowledge of German language
- Experience in project coordination
- Experience in research activities, in the preparation of scientific 
reports and publications or activities as a student or research assistant

---------------------------------------------------------------------------------------------------------------

Benefits:
- 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

---------------------------------------------------------------------------------------------------------------

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.-Ing. Wolfram Schenck via e-mail at 
wolfram.schenck[AT]fh-bielefeld.de.

** Are you interested? Please apply online only by 23 February 2023, 
stating the reference number 03302. **

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


-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20230131/24aa3b4a/attachment.html>