- Department / Institute
- Institute of Informatics
- Subject area
- Computer Science – Research in Data Mining
- Language requirements
- Very good English communication and writing skills are expected.
- Academic requirements
- Candidates must hold a M.Sc. degree in Informatics, Medical‐ or Bioinformatics or related disciplines like Information Technology, Electrical Engineering, and Statistics. We search for candidates who want to qualify for a scientific career in the field of database‐related data mining.
Successful candidates have a high level of algorithmic and mathematic creativity combined with a strong interest in biomedical research questions. The positions require a high level of motivation, team‐ orientation and dedication to interdisciplinary work well as the ability to solve problems independently.
Programming skills preferably in Java and Matlab are expected.
Candidates should have excellent grades and should be awarded best student prizes.
Qualified women are explicitly invited to apply. We are also interested in couples where both partners have a strong background in computer science and are interested in Data Mining Research. If the spouse has a strong background in a different discipline, we are helpful for finding a place at one of our cooperation institutions.
- Please send your application including CV and list of publications to Prof. Christian Böhm:
We are seeking for excellent candidates having a CSC scholarship who are interested in the field of data mining with a particular focus on algorithms and applications from life sciences, medicine, etc. In particular, we are interested in persons with a grant from the LMU‐CSC program, see also:
Our research focuses on innovative data mining methods. We perform cutting‐edge research on the topics of clustering and information‐theoretic data mining.
To comprehensively support high‐level biomedical research questions we will be working on two major goals in the near future:
1) The generalization of several basic approaches to explorative data analysis including clustering and feature selection and feature transformation.
2) The integration of different data types including numerical feature vectors, categorical and ordinal variables as well as hierarchical and graph‐structured information. This research will be carried out in intense interdisciplinary collaboration with leading biomedical experts investigating a comprehensive environmental health approach to better understand complex lifestyle‐related diseases.