DC10 PhD offer in the consortium DONUT at the Institute of Measurement Science of the Slovak Academy of Sciences: “Towards an ecologically valid BCI and head-mounted VR system for post-stroke neurorehabilitation”

Offer Description

detailed offer here: https://euraxess.ec.europa.eu/jobs/183375

The European Doctoral Network for Neural Prostheses and Brain Research (DONUT) is a 4-years EU-Horizon Europe Marie Sklodowska-Curie Doctoral Network project that is aimed to bring together leading experts from several European universities with the mission to provide a multidisciplinary and intersectoral Doctoral Network for talented young researchers (Doctoral Candidates). The network connects leading scientists and institutions with several industries over different research fields, providing opportunity for young researchers to gain experience in translational research in electroencephalography (EEG)-based measurements and Brain-Computer Interface (BCI) applications, healthcare, and industry.

PhD project description:

A growing body of evidence suggests that intelligent systems based on brain-computer interfaces (BCI) provide a flexible platform for a series of neurorehabilitation therapies. Additionally, the recent development of augmented or virtual reality (VR) technologies moved BCI forward by creating flexible environments where modern human-computer interactions are necessary. Head-mounted VR displays (HMD) move standard 2D screen-based VR into a virtual 3D world. This shift brings another level of flexibility and enables the creation of immersive virtual worlds.

The dissertation is focused on the area of motor neurorehabilitation in patients after stroke. Rehabilitation takes place using the BCI-HMD system, which consists of BCI and feedback in the form of visualization in a VR environment. The dissertation’s practical part aims to improve the software and hardware part of the existing system to create a robust, compact, easily applicable, and user-acceptable system. This section focuses on measuring, evaluating, and analyzing electrical brain activity through scalp-recorded electroencephalography (EEG). The second part of the dissertation is focused on developing advanced machine learning (ML) tools and algorithms for detecting EEG changes associated with mental imagery of movement and changes in subjects’ mental and cognitive states during BCI training. Attention will be paid to understanding the brain processes related to activities in the BCI-HMD environment. This goal will be achieved by carefully balancing existing neurophysiological evidence and knowledge with the information provided by ML tools that process recorded brain activity, and it will be verified through detailed experimental testing.


Research Field
Engineering » Biomedical engineering
Education Level
Master Degree or equivalent

Please carefully review the following requirements that candidates must meet for this PhD project. Each requirement holds equal importance:

Scientific Skills and Research Experience:

  • Familiarity with modern methods of applied statistics and machine learning.
  • Familiarity with modern Brain-Computer Interface (BCI) technology.
  • Familiarity with basic principles of signal processing techniques applied to human scalp EEG.
  • Proficiency in scientific writing is a crucial aspect of the PhD.
  • Previous experience working in a laboratory setting.
  • Possession of a broad scientific background.

Educational Qualifications:

  • Hold an MSc degree in fields relevant to the PhD project—biomedical engineering, computer science, applied statistics or informatics, computational neuroscience, or related.

Language Proficiency:

  • Exhibit a good command of English, both in written and spoken forms.

Team Collaboration:

  • Comfort and willingness to work collaboratively in a group setting within the laboratory.
  • Ability to contribute to common projects, share experimental results, and learn from colleagues.


  • Display high motivation to actively participate in the 3-year PhD program leading to a doctoral degree.


  • Willingness to engage in mandatory secondments between members of the DONUT consortium.
  • Ability to work independently when required.

Initiative and Proactivity:

  • Demonstrate a proactive and highly initiative approach to tasks and challenges.

Preferred Experience:

  • Previous experience in research labs will be highly valued.

Ensuring compliance with all these requirements is essential for successful consideration as a candidate for this PhD position.

Specific Requirements
  • Highly motivated, independent, and enthusiastic doctoral researcher.
  • Strong background in biomedical engineering, computational methods, and neurosciences.
  • Excellent track record of academic achievement and a strong interest in conducting original research and innovation.
  • Good programming skills, including experience with MATLAB, Python, and related.
Languages         ENGLISH
Level                   Good