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Professorship (W1) of "Machine Learning"
Technische Universität Kaiserslautern
The Department of Computer Science at the Technische Universität Kaiserslautern invites
applications for the position of a
W1 Professor of „Machine Learning“
(Juniorprofessor funded by the
Carl Zeiss Stiftung)
We are seeking candidates with research focus in core machine learning as evidenced by
publications in conferences and journals such as, e.g., NeurIPS, ICML, COLT, ALT, ICLR,
AISTATS, UAI, JMLR, MLJ, TNNLS, or TPAMI. Prime candidates would be open toward
applications of machine learning, as ideally (but not necessarily) demonstrated already
through publications at conferences or journals in application areas of machine learning,
such as, e.g., computer vision (e.g., CVPR, ICCV), natural language processing (e.g., ACL),
robotics (e.g., IROS), or the natural sciences and engineering.
Kaiserslautern is one of the largest computer science locations in Germany. The department
combines foundations in education with excellence in applications, and regularly ranks
among the top universities in German rankings. More information about the Department
of Computer Science can be found at http://www.informatik.uni-kl.de/en/. Research groups
of the department of computer science have the opportunity to cooperate with its affiliated
research institutes, i.e., the Fraunhofer Institute for Experimental Software Engineering
(IESE), the Fraunhofer Institute for Industrial Mathematics (ITWM), the German Research
Center for Artificial Intelligence (DFKI), and the Max Planck Institute for Software Systems
(MPI-SWS). This variety of research institutes in Computer Science is unique throughout
Successful candidates will develop an independent and creative research program,
participate in teaching at master level (in English) at the department, and supervise PhD
students. The Juniorprofessor is expected to lead the new junior research group on
machine learning and physical modelling at the department of computer science. Regarding
personnel, she or he receives substantial resources. The group works on ground-breaking
applications of machine learning in chemical engineering, in tandem with another such
group at the department of mechanical and chemical engineering of TU Kaiserslautern.
The Juniorprofessor will be given the opportunity to be integrated into and collaborate with
the existing machine learning group at the department (http://ml.cs.uni-kl.de), from which
she or he will receive full support. The juniorprofessor‘s group receives significant start-up
resources and access to outstanding research infrastructure, in particular, up-to-date GPUsupercomputing
facilities. No German language skills are required. For further information
please contact Prof. Dr. Marius Kloft (email@example.com).
Note that, in addition to public service employment regulations, the conditions of
employment regulated in §54 of the Universities Act of the state Rhineland-Palatinate apply.
The text can be found on the homepage of the University of Kaiserslautern (in German, only)
and the University of Kaiserslautern support a mentoring concept in which teachers are
expected to be present on campus as much as possible. Candidates must be willing to
cooperate with the self-administration at the university. The University of Kaiserslautern
strongly encourages qualified female academics to apply. Kaiserslautern is a family friendly
university which offers a high level of support to applicants with children. Disabled applicants
will be given preference when equally qualified (please enclose evidence).
With your application, you consent to the further internal processing of your data for official
purposes in accordance with the European Data Protection Basic Regulation (DS-GVO) and
the State Data Protection Act RLP.
To apply, please send your application until 15 January 2020 in a single PDF file to firstname.lastname@example.org or via postal mail to the following address:
Dean of the Department of Computer Science
Technische Universität Kaiserslautern
PO Box 3049
The following information is requested, either in German or English (English is preferred):
completed candidate data sheet (http://cs.uni-kl.de/en/goto/w1-ml), cover letter, CV
(education, appointments, awards, community service), list of all publications and selected
talks, list of (e.g., fi ve) representative publications (including electronic link or pdf attached),
grants (if applicable), research statement, teaching portfolio (optional), course evaluations
(if applicable), trainings (if applicable), certifi cates, list of international collaborators and their
affi liation, and references (or their contact information).
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