We are looking for a PhD candidate to set up closed-loop brain-computer interface protocols and to evaluate brain signals and the task performance of users. This position needs to be filled as soon as possible.

Neurotechnological systems such as brain-computer interfaces (BCIs) allow to record and interpret the ongoing brain activity of healthy users or patients. This allows to design closed-loop applications for monitoring, for communication, for the control of devices or to support rehabilitation training. As brain signals are individual, noisy, and high-dimensional, machine learning methods play a crucial role in these systems.

Using a BCI system is not a natural skill. Thus not only the computer, but also the user undergoes a learning phase in order to produce more discriminative brain signals. In the case of BCI-supported rehabilitation training, this may comprise learning how to better use brain networks spared by a stroke, or how to generate specific brain responses that can be recognised by artificial intelligence methods. The PhD project investigates how healthy users and/or patients can be supported in the longitudinal learning of a BCI skill. The focus will be on how both experimental protocol design and machine learning methods can be optimised to provide enriched feedback in order to support the user’s self-introspection.

You will be expected to design and implement experimental protocols in Python to study the introspection ability of BCI users and the role of feedback. In our own labs or in clinics, you will conduct non-invasive EEG studies with healthy participants and patients, and cooperate with our clinical partners. Furthermore, you will train machine learning models to analyse the data and participate in the scientific dissemination of results in high-impact scientific journals, conferences and workshops. In addition we expect an attitude towards open and reproducible science, which includes the publishing of well-documented code and FAIR datasets. An excellent command of English is required, as this is the working language in our international lab.

We offer a full time position for an overall duration of four years. Throughout the project, you will receive guidance from Dr Michael Tangermann and Dr Jordy Thielen and be an integral part of the Data-Driven Neurotechnology Lab. The lab is situated within the AI department of the Donders Institute, offering additional opportunities for collaboration with experts in artificial intelligence, cognitive neuroscience, visual perception, and other relevant fields. You will also benefit from the extensive training programmes offered by the Donders Graduate School and the European doctoral training network DONUT (https://donut-project.eu/). In addition, you will benefit from multi-month research stays at DONUT’s academic and industrial partners’ labs, which require mobility.

You must comply with the following mobility rule: you must not have resided or carried out their main activity (work, studies, etc.) in the Netherlands for more than 12 months in the 36 months immediately before the recruitment date. You will be part of the Donders Graduate School for Cognitive Neuroscience and the European Doctoral Network for Neural Prostheses and Brain Research.

Your teaching load may be up to 10% of your working time.