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PhD position: Closed loop control for deep brain stimulation

We have an open PhD position for developing a closed-loop system for deep brain stimulation!

Our group investigates machine learning methods to realize data-driven, individualized neurotechnological systems. Deep brain stimulation (DBS) is an established therapy for Parkinson’s disease (PD). Currently, after electrode implantation, DBS parameters (for example, amplitude or frequency) are tuned by hand by an expert clinician. This infrequent tuning has been found  insufficient to adapt to fast changes in the disease progress or varying symptoms intensity due to e.g. medication.

These challenges call for research on closed-loop DBS systems that allow to adapt stimulation parameters in real time as a function of brain signals such as EEG, ECoG or LFPs. To this end, we seek to develop a control system (based for example on reinforcement learning) able to cope with complex non-stationary brain dynamics, which in the context of PD, are mainly caused by disease progress, stimulation, and medication.

We investigate such closed-loop approaches in collaboration with our partners at the University Medical Center Freiburg---where the implantation surgeries and inpatient visits take place---and with a company focussing on neural implant technology.

Do you want to know more? Find here the full description of the position

Student assistant for machine learning tasks related to Brain-Computer Interfaces

We are looking for a highly motivated student (preferably master-level) with a background in computer science and machine learning and a high interest in brain-computer interfaces.