Phd Position: Role of User Feedback and

Employment
1.0 FTE
Gross monthly salary
€ 2,770 - € 3,539
Required background
Research University Degree
Organizational unit
Faculty of Social Sciences
Application deadline
12 September 2024
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.
**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.
Profile
You have obtained a Master's degree in Artificial Intelligence, Computer Science, Cognitive Neuroscience or a related discipline.
You have a strong interest in neurotechnological systems such as brain-computer interfaces.
You have a strong interest in neuroscience and self-introspection.
You have a passion for research.
You have enthusiasm and the ability to work in an international team.
You have an excellent background in mathematics and machine learning.
You have strong Python programming skills, and you are familiar with libraries for neuroimaging data as well as for machine learning.
You know how to collaborate on larger software projects and are familiar with tools and infrastructure such as compute clusters, version control, etc.
You have experience with dealing with neuroscientific data (e.g. from EEGs).
You possess excellent written and spoken English language skills.
We are
The Donders Institute for Brain, Cognition and Behaviour is a world-class interfaculty research centre that houses more than 700 researchers devoted to understanding the mechanistic underpinnings of the human mind. Research at the Donders Institute is focused around four themes: 1. Language and communication, 2. Perception, action and control, 3. Plasticity and memory, 4. Neural computation and neurotechnology. Excellent, state-of-the-art research facilities are available for the broad range of neuroscience research that is being conducted at the Donders Institute. The Donders Institute has been assessed by an international evaluation committee as 'excellent' and recognised as a 'very stimulating environment for top researchers, as well as for young talent'. The Donders Institute fosters a collaborative, multidisciplinary, supportive research environment with a diverse international staff. English is the lingua franca at the Institute.
The Data-Driven Neurotechnology Lab is embedded in the Donders Institute and in the Department of Artificial Intelligence. The lab's research focuses on data-driven neurotechnology, i.e. domain-specific machine learning methods which allow to decode brain states in a single trial and methods which permit to modulate the ongoing brain state.
Radboud University
At Radboud University, we aim to make an impact through our work. We achieve this by conducting groundbreaking research, providing high-quality education, offering excellent support, and fostering collaborations within and outside the university. In doing so, we contribute indispensably to a healthy, free world with equal opportunities for all. To accomplish this, we need even more colleagues who, based on their expertise, are willing to search for answers. We advocate for an inclusive community and welcome employees with diverse backgrounds, cultures, and perspectives. Will you also contribute to making the world a little better? You have a part to play.
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