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CC15 Neurology, Bernstein Center for Computational Neuroscience;
PhD in machine learning / signal processing / neuroimaging within the ERC-funded project
area of responsibility
- The topic of this PhD project is EEG / MEG functional connectomes in aging and dementia.
- Understanding the mechanisms through which brain regions communicate in health and disease is one of the key research directions in neuroscience. The high temporal resolution of EEG and MEG enables the study of important types of non-linear functional brain connectivity (eg, PAC / AAC) but their low spatial resolution leads to false-positive detections of brain interactions by current methods to develop a robust study of non-linear functional connectivity in neuroimaging data (EEG, MEG, intracranial recordings). The work also comprises
- to carry out computer simulation to validate methods.
- to publish data analysis pipelines as user-friendly open-source toolboxes written in Matlab / Python.
- Robust functional connectivity in aging and dementia.
- to apply machine learning algorithms to derive interpretable predictions of clinical and behavioral variables from EEG / MEG FC.
- to conduct literature surveys, co-organizing workshops.
- to publish research results in relevant scientific journals and present results as talks / posters at relevant conferences.
- See braindata.charite.com for further information on the position and research group.
- Very good diploma, MSc, or equivalent degree in a technical field (eg, machine learning, computer science, statistics, mathematics, computational (neuro) science, data science, physics, electrical / biomedical engineering)
- Strong interest in computational methods (machine learning, signal processing) and neuroscience
- Solid background in mathematics / statistics
- Good coding skills (eg, Matlab, Python, C ++, Java)
- Very good command of written English
- Prior experience with functional neuroimaging data is a plus
- Applications should include a letter of motivation, a CV, transcripts and degree certificates, as well as (if available) references, an English-language writing sample, and a coding sample (eg link to a github project). Applications should be sent by email to email@example.com quoting the reference number. All documents should be in a single pdf.