Two W3-Professorships for Machine Learning
Technische Universität Kaiserslautern (TUK) is a technology and science-oriented research university with an international profile. It is the center of the up-and-coming IT site in the heart of the Palatinate region, which is positioning itself with a future-oriented focus on software technologies and artificial intelligence, especially in the field of machine learning.
A strong network and driving force in the field of AI
The German Research Center for Artificial Intelligence (DFKI), founded in 1988 from the TUK and represented there since then, is a leader in the research and application of artificial intelligence. Further cooperation partners of TUK are the neighbouring Fraunhofer Institute for Industrial Mathematics (ITWM), the Fraunhofer Institute for Experimental Software Engineering (IESE) and the Max Planck Institute for Software Systems (MPI-SWS) as well as innovative companies - on site, nationally and internationally. Together with the research institutes, TUK forms a campus university with short distances and a cooperative partnership.
Together with the research institutes, TUK forms a tight-knit, collaborative AI campus.
As a central component of the AI strategy of the state of Rhineland-Palatinate, the following two professorships in the Department of Computer Science at the TUK are to be filled as soon as possible
Professor (W3) for “Applied Machine Learning” (m/f/d)
Professor (W3) for “Fundamental methods in Machine Learning” (m/f/d)
We are seeking internationally visible candidates with research focus in machine learning (ML). The professorship “Applied Machine Learning” will be combined with an additional scientific director position at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. The professorship “Fundamental methods in Machine Learning” is primarily rooted at the Department of Computer Science. However, collaboration with researchers across institutes and the department is possible and also anticipated.
For both professorships, topics of interest include the following aspects of ML and deep learning: explainability and interpretability, data fusion, trustworthiness, anomaly detection, fairness, extreme classification, stochastic optimization, learning theory, generative modeling, representation learning, Bayesian ML, and reinforcement learning. Applications of ML and deep learning of interest include, e.g., the following: life science, fintech, climate change and sustainability, social science, and mechanical or process engineering.
Outstanding academic qualifications, as evidenced by a proven record of high-quality publications in renown machine-learning (e.g., NeurIPS, ICML, ICLR, AAAI, COLT, ECML, ACML, JMLR, TPAMI, MLJ, TNNLS, CVPR, ECCV, etc.) or application-specific venues, experience in third-party funding and the implementation of projects, documentation of teaching skills, and excellent English language skills.
Successful candidates represent their discipline in teaching and research, will develop an independent and creative research program, participate in teaching at bachelor and master level (in English) at the department, supervise PhD students, and participate in the university self-administration. Candidates are expected to be willing to collaborate with existing research groups at the university (disciplinary or interdisciplinary) and support the research of the department in at least one of its main research areas.
Requirements for Employment:
In addition to public service employment regulations, the conditions of employment regulated in §49 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) ( https://www.uni-kl.de/intern/meine-tuk/hauptabteilung-1/rechtsvorschriften/). Rhineland-Palatinate 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.
How to apply:
Please send your application until 31. December 2020 in a single PDF file to firstname.lastname@example.org. Alternatively, you can ask for an upload link or you can send your application via postal mail to the following address:
Dean of the Department of Computer Science,
Technische Universität Kaiserslautern
PO Box 3049,
Please specify in the cover letter to which professorship(s) you apply to. The following information is requested, either in German or English (English is preferred): completed candidate data sheet (https://cs.uni-kl.de/en/goto/w3-ml), cover letter, CV (education, appointments, awards, community service), list of all publications and selected talks, list of (e.g., five) representative publications (including electronic link or pdf attached), grants, research statement, teaching portfolio (optional), course evaluations, trainings (if applicable), certificates, list of international collaborators and their affiliation, and references (or their contact information).
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.
TUK is a family-friendly university. It values the diversity of its members and enables equal opportunities for all.
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).
For further information, please contact
Professorship for “Applied Machine Learning”:
Prof. Dr. Prof. h.c. Andreas Dengel (email@example.com).
Professorship for “Fundamental methods in Machine Learning”:
Prof. Dr. Marius Kloft (firstname.lastname@example.org).