Dear all, PhD positions for highly motivated graduate students are available in the ERC funded research group of Stefan Haufe at the Berlin Center for Advanced Neuroimaging (BCAN) of the Charité - University Medicine Berlin, Germany. The group will develop machine learning and signal processing methods for the analysis of non-invasive brain signals (primarily EEG/MEG) and apply these methods to patient data with the aim of better understanding neurological diseases such as Parkinson's and Alzheimer’s disease. Appointments can be made for up to four years starting from January 2019 or later. Remuneration is based on the German pay scale TVL E13 (65% working time). Candidates are required to hold a very good MSc or equivalent degree in a relevant field and are expected to demonstrate extraordinary skills in at least one of the following core areas A: Mathematics and quantitative data analysis/modeling (machine learning, signal processing, statistics, numerical optimization, etc.). Experience with efficient algorithms and large data sets. B: Neuroscience (psychology, cognitive science). Knowledge of general research practices and experience in the design, execution, and statistical evaluation of studies. C: Computer science. Experience in software development, programming in Matlab/Python/C, system administration. Further required are excellent communication skills in English (written and spoken), and a genuine interest in interdisciplinary work. Prior experience with functional neuroimaging data is a plus. Successful candidates will work at Charité's historic Mitte campus in the center of Berlin, and will be embedded in a stimulating research environment. Applications should be sent by email to stefan.haufe@charite.de quoting the reference number DM.143.18b. In addition to cv and cover letter, applicants are encouraged to include further documentation such as references, transcripts, copies of bachelor/master theses/research papers (in English or German), links to github repositories, etc. . All documents should be contained in a single pdf.