Charité - Universitätsmedizin Berlin - Department of Psychiatry and Psychotherapy, Group: Machine Learning in Clinical Neuroimaging
The job position is in the new research unit 5187 "Towards precision psychotherapy for non-respondent patients: From signatures to predictions to clinical utility" (lead: Prof. Ulrike Lüken, Humboldt-Universität zu Berlin) in the framework of the subproject "Methods development" (lead: Prof. Kerstin Ritter and Prof. John-Dylan Haynes).
Research Assistant (PostDoc)
Charité Campus Mitte
Develop novel methods at the intersection of machine learning / deep learning and neuroimaging
Develop new data analysis methods for multimodal data (clinical data, smartphone data, EEG data, MRI and fMRI data)
Carry out computer simulations to validate methods
Publish data analysis pipelines as user-friendly open-source toolboxes written in Python
Analyze patient data to address clinical research questions in psychiatric diseases (depression, anxiety etc)
Design and conduct own experiments
Conduct literature surveys
Publish research results in relevant scientific journals and present results as talks/posters at relevant conferences
Scientific staff is given sufficient time to carry out their own scientific work in accordance with their employment relationship.
Excellent degree (Diploma, MSc, ... + PhD) in relevant field (e.g. computer science, mathematics, physics, psychology, computational neuroscience or related)
Excellent methodological skills (esp. machine learning, deep learning, explainable AI, medical image analysis)
Excellent programming skills in python
Excellent writing and communication skills (in English)
Strong interest in neuroscientific and psychiatric research
Applications are expected to include a letter of motivation and a CV. Publications written in either English or German (such as MSc theses, research project reports) and links to software projects, git-hub repositories may also be included. All documents should be contained in a single pdf file.