PhD and Postdoc positions in Statistical Machine Learning at the Machine Learning Group, Department of Computer Science, TU Kaiserslautern

The machine learning group at the Department of Computer Science
at TU Kaiserslautern (headed by Prof. Marius Kloft,
https://www2.informatik.hu-berlin.de/~kloftmar/)
is recruiting highly-motivated doctoral and postdoctoral researchers.
The research group, which was established at HU Berlin in 2014,
is moving from HU Berlin to TU Kaiserslautern on 1 Oct 2017.
*ABOUT THE GROUP*
The group develops theoretically grounded statistical machine
learning methods for analysis of big data. The group has
developed effective learning methods and statistical learning
theory for integrating the information from multiple sensor
types (multiple kernel learning) or multiple learning
tasks (multi-task learning) as well as anomaly detection.
The group has successfully applied these methods in various
application domains, including network intrusion detection
(Remind system), visual image recognition (ImageClef Challenge
winner), computational biology (most accurate gene start finder),
computational medicine (most accurate drug sensitivity predictor)
and smart agriculture.
The group's most recent research focuses on deep learning,
deep anomaly detection, Bayesian learning methods for big data,
and extreme classification (multi-class or multi-label learning
involving an extremely large number of label classes).
*ABOUT THE LOCATION*
Beautifully set in the UNESCO-designated Palatinate Forest -
North Vosges Biosphere Reserve (biggest forest of Europe),
Kaiserslautern is one of the largest IT locations in Germany
('Silicon Woods'), with the Department of Computer Science
(24 professors) at Kaiserslautern Institute of Technology
(TU Kaiserslautern) forming its core.
TU Kaiserslautern has a strong focus on artificial intelligence,
machine learning, and data science, with the German Research
Center for Artificial Intelligence (one of the world's largest
nonprofit contract research institutes for software technology
based on artificial intelligence), the Max-Planck Institute (MPI)
for Software Systems, and the Fraunhofer Institute for Experimental
Software Engineering being located at the same campus.
*HOW TO APPLY*
The candidates are expected to conduct fundamental machine
learning research and contribute to ongoing projects.
Successful candidates can be given the opportunity to contribute
to teaching and to co-advise undergraduate and M.Sc. students
and work with other PhD students and Postdocs.
Candidates are expected to contribute by fundamental ML research
(that is, developing new algorithms or theory) to one of the
groups' focuses (described above). The group draws from a
network of international collaboration partners (academic and
industrial) in Europe, US, and Asia. Collaboration with the
partners is encouraged.
Applications are submitted by email to
kloft-AT-cs.uni-kl.de
using subject
'Application for the PhD Position KL-ML-PhD"
or
'Application for the Postdoc Position KL-ML-PostDoc"
Applications shall include a link to the supporting documents,
which are asked to include a signed cover/motivation letter
mentioning the job ad number, a CV, supporting letters or
references, a pdf of the BSc and MSc theses, evidence for
mathematical skills (e.g., grades, mathematical thesis, etc.),
and - ideally - a research statement (mandatory for postdoc
applications).
The final contract will given to successful applicants
by the recruitment department of TU Kaiserslautern.
The duration of the contract depends on the position, the
contract, and the funding source. Payment is according
to the competitive German TVL E-13 payment scheme,
depending on the candidate's experience and qualifications.
Applicants are cordially invited to contact Marius Kloft
informally by email for further inquiries. Please use the
subject stated above.