Connectionists: 12 Research Positions - Joint Lab for AI & Data Science of the Leibniz Institute ATB and Osnabrück University

[Kai-Uwe Kühnberger]

The Joint Lab for Artificial Intelligence & Data Science of the Leibniz 
Institute for Agricultural Engineering and Bioeconomy e. V. and 
Osnabrück University is establishing a Research Training Group. The 
associated partners are Agrotech Valley Forum, German Research Center 
for Artificial Intelligence (DFKI)  and Osnabrück University of Applied 
Sciences. The core objective of the Joint Lab is to develop Artificial 
Intelligence (AI) & Data Science (DS) expertise, in particular for 
agricultural technology systems.
You are a passionate computer scientist or applied mathematician, 
intrinsically motivated to contribute your expertise to a societally 
highly-relevant research field?
Or do you have a background in agricultural engineering, environmental 
or natural sciences with a keen interest in the field of AI & Data 
Science? Then apply now and contribute to excellent research in 
agriculture, food economy, and bioeconomy.

For the Research Training Group, the Joint Lab for Artificial 
Intelligence & Data Science is looking for


      *12 Research Assistants (m/f/d)
      (Salary level E 13 TV-L, 100%)*

All positions are for a period of four years, starting as soon as possible.

The application process is two-stage. You apply to a job pool, 
indicating your competencies and interests. After a preliminary 
assessment of fit, suitable applicants are invited for job interviews.


*Your tasks:*

  * Conducting scientific research on the intersection of (explainable)
    Artificial Intelligence and Data Science in Bioeconomic Systems
  * Contributing to research with the aim of obtaining a doctorate degree
  * Preparation of project reports and scientific publications
  * Presentation of project results at conferences and workshops

*Required qualifications:*

  * Above-average academic degree (Master's or equivalent) in computer
    science, engineering, mathematics, environmental systems science,
    natural sciences, or related fields of study
  * In-depth knowledge in at least one of the relevant areas:
    Agricultural Robotics, Applied Multivariate Statistics, Data
    Aggregation, Data Driven Process Modeling, Deep Learning, Digital
    Twins, Domain Specific Hardware Architectures, (Explained)
    Artificial Intelligence, (Informed) Machine Learning, Navigation and
    Environment Recognition, Object Recognition, Recommender Systems,
    Sensor Data Fusion, Control Systems
  * First practical experience in the development and application of
    Machine Learning algorithms
  * Programming skills (e.g. in Python) and first experience with ML and
    corresponding libraries (PyTorch, Tensorflow, NumPy, sklearn, etc.).
  * Ideally, experience with versioning tools, such as Git, and
    unix-based systems, such as Linux
  * Very good English language skills (written and spoken), German
    language skills are a plus
  * Flexibility, creativity and strong communication skills
  * High sense of responsibility, reliability, personal commitment and
    goal-oriented and independent work as well as scientific ambitions

*We offer: *

  * Exciting research tasks in the field of AI & Data Science with
    highly relevant societal application fields
  * The opportunity to publish your papers in conference and journal
    publications
  * The possibility to obtain a doctorate degree
  * A highly motivated and international team as part of the Research
    Training Group
  * Interdisciplinary doctoral supervision ensured by teams from
    Osnabrück and Potsdam
  * Flexible work hours and excellent equipment
  * Broad selection of topics from the following areas, among others:

      o Artificial intelligence, explainable AI, computer vision,
        knowledge representation
      o Causal data analysis in complex agricultural systems
      o Intelligent recommender systems, multi-parameter optimization
      o Data-driven process modeling and analysis of complex systems
      o Efficient/resource-constrained sensor data acquisition and fusion
      o Domain-specific, resource-efficient, adaptive hardware architectures
      o Distributed systems, mobile systems with limited energy budget
      o Development of project-specific infrastructure, digital twins
      o Informed Machine Learning (Physics-Informed Machine Learning).
      o Agricultural robots, control, navigation, environment detection,
        functional safety

For more details see www.jl-kids.uos.de <http://www.jl-kids.uos.de>.

The Joint Lab connects the two locations Osnabrück and Potsdam, a 
willingness to travel is therefore required. The PhD students are 
supervised by a team of professors and scientists from Osnabrück and 
Potsdam.

Reference is made to the possibility of part-time employment.

As a family-friendly university, the University of Osnabrück is 
committed to the compatibility of work / study and family.

The Osnabrück University particularly wants to promote the professional 
equality of women and men. Therefore, it strives to increase the 
proportion of the gender that is underrepresented in the respective field.

Severely handicapped applicants or persons of equal status will be given 
preferential consideration in the event of equal suitability.

Please send your complete documents (Curriculum vitae, certificates, 
cover letter) exclusively in electronic form (in a PDF file) and 
separately enclose the form "Application profile (DOCX, 13,01 kB) 
<https://www.uni-osnabrueck.de/fileadmin/documents/public/1_universitaet/1.3_organisation/d2_personal/d2_personal/downloads/stellenangebote/60_FB_6_Research_Assistants_Application_profile.docx>" 
by the date *March 30, 2023* to the email 
address:jl-kids at uni-osnabrueck.de <mailto:jl-kids at uni-osnabrueck.de>.

We are looking forward to your application.

For further information, please contact Professor Dr. Tim Römer (Tel. 
0541 969 – 2545, tim.roemer at uni-osnabrueck.de 
<mailto:tim.roemer at uni-osnabrueck.de>) or Professor Dr. Martin Atzmüller 
(martin.atzmueller at uni-osnabrueck.de 
<mailto:martin.atzmueller at uni-osnabrueck.de>).
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