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<p><br>
The Joint Lab for Artificial Intelligence & Data Science of
the Leibniz Institute for Agricultural Engineering and Bioeconomy
<abbr title="eingetragener Verein">e. V.</abbr> and Osnabrück
University is establishing a Research Training Group. The
associated partners are Agrotech Valley Forum, German Research
Center for Artificial Intelligence (<abbr title="Deutsche
Forschungszentrum für Künstliche Intelligenz">DFKI</abbr>) 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.<br>
You are a passionate computer scientist or applied mathematician,
intrinsically motivated to contribute your expertise to a
societally highly-relevant research field?<br>
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.</p>
<p>For the Research Training Group, the Joint Lab for Artificial
Intelligence & Data Science is looking
for </p>
<h3><strong>12 Research Assistants (m/f/d)<br>
(Salary level E 13 TV-L, 100%)</strong></h3>
<p>All positions are for a period of four years, starting as soon as
possible.</p>
<p>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.</p>
<p><br>
<strong>Your tasks:</strong></p>
<ul>
<li>Conducting scientific research on the intersection of
(explainable) Artificial Intelligence and Data Science in
Bioeconomic Systems</li>
<li>Contributing to research with the aim of obtaining a doctorate
degree</li>
<li>Preparation of project reports and scientific publications</li>
<li>Presentation of project results at conferences and workshops</li>
</ul>
<p><strong>Required qualifications:</strong></p>
<ul>
<li>Above-average academic degree (Master's or equivalent) in
computer science, engineering, mathematics, environmental
systems science, natural sciences, or related fields of study</li>
<li>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</li>
<li>First practical experience in the development and application
of Machine Learning algorithms</li>
<li>Programming skills (<abbr title="for example">e.g.</abbr> in
Python) and first experience with ML and corresponding libraries
(PyTorch, Tensorflow, NumPy, sklearn, <abbr title="et cetera">etc.</abbr>).</li>
<li>Ideally, experience with versioning tools, such as Git, and
unix-based systems, such as Linux</li>
<li>Very good English language skills (written and spoken), German
language skills are a plus</li>
<li>Flexibility, creativity and strong communication skills</li>
<li>High sense of responsibility, reliability, personal commitment
and goal-oriented and independent work as well as scientific
ambitions</li>
</ul>
<p><strong>We offer: </strong></p>
<ul>
<p> </p>
<li>Exciting research tasks in the field of AI & Data Science
with highly relevant societal application fields</li>
<li>The opportunity to publish your papers in conference and
journal publications</li>
<li>The possibility to obtain a doctorate degree</li>
<li>A highly motivated and international team as part of the
Research Training Group</li>
<li>Interdisciplinary doctoral supervision ensured by teams from
Osnabrück and Potsdam</li>
<li>Flexible work hours and excellent equipment</li>
<li>Broad selection of topics from the following areas, among
others:
<p></p>
<ul>
<li>Artificial intelligence, explainable AI, computer vision,
knowledge representation</li>
<li>Causal data analysis in complex agricultural systems</li>
<li>Intelligent recommender systems, multi-parameter
optimization</li>
<li>Data-driven process modeling and analysis of complex
systems</li>
<li>Efficient/resource-constrained sensor data acquisition and
fusion</li>
<li>Domain-specific, resource-efficient, adaptive hardware
architectures</li>
<li>Distributed systems, mobile systems with limited energy
budget</li>
<li>Development of project-specific infrastructure, digital
twins</li>
<li>Informed Machine Learning (Physics-Informed Machine
Learning).</li>
<li>Agricultural robots, control, navigation, environment
detection, functional safety</li>
</ul>
<p> </p>
</li>
<p></p>
</ul>
<p>For more details see <a href="http://www.jl-kids.uos.de"
target="_blank" class="external-link">www.jl-kids.uos.de</a>.</p>
<p>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.</p>
<p>Reference is made to the possibility of part-time employment.</p>
<p>As a family-friendly university, the University of Osnabrück is
committed to the compatibility of work / study and family.</p>
<p>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.</p>
<p>Severely handicapped applicants or persons of equal status will
be given preferential consideration in the event of equal
suitability.</p>
<p>Please send your complete documents (Curriculum vitae,
certificates, cover letter) exclusively in electronic form (in a <abbr
title="Portable Document Format">PDF</abbr> file) and separately
enclose the form "<a
href="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"
target="_blank" class="download">Application profile (DOCX,
13,01 <abbr title="Kilobyte">kB</abbr>)</a>" by the date <strong>March
30, 2023</strong> to the email address:<a
href="mailto:jl-kids@uni-osnabrueck.de" class="mail
moz-txt-link-freetext"> jl-kids@uni-osnabrueck.de</a>.</p>
<p>We are looking forward to your application.</p>
<p>For further information, please contact Professor Dr. Tim Römer
(Tel. 0541 969 – 2545, <a
href="mailto:tim.roemer@uni-osnabrueck.de" class="mail
moz-txt-link-freetext">tim.roemer@uni-osnabrueck.de</a>) or
Professor Dr. Martin Atzmüller (<a
href="mailto:martin.atzmueller@uni-osnabrueck.de" class="mail
moz-txt-link-freetext">martin.atzmueller@uni-osnabrueck.de</a>).</p>
<p></p>
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