University Assistant (prae doc)

- A successfully completed master's degree or equivalent in Computer Science, Business Informatics, or a closely related field is required, whereby knowledge of computer science is assumed to be a given
- Methodological competence in machine learning methods, to design and implement algorithms for offline and online classification
- Professional competence in designing and conducting EEG studies
- Methodological competence in analyzing brain signals, such as signal processing methods for investigating EEG features
- Didactic competence, teaching experience / experience of working with e-learning
- High ability to express yourself both orally and in writing
- Excellent command of written and spoken English
- Basic experience in research methods and academic writing
- IT user skills
- Ability to work in a team
- Knowledge of university processes and structures
Desirable qualifications are

- Methodological competence in developing and implementing Riemannian geometry-based transfer learning models

- Experience with Virtual Reality (VR), including the design and implementation of rehabilitation tasks

- Practical competence in designing and building an EEG-based BCI that combines post-stroke motor rehabilitation and neurofeedback in VR

- Experience with working with stroke patients

- Experience abroad

Application documents

- Letter of Motivation including ideas for a prospective doctoral project proposal

- Curriculum vitae

- List of publications, evidence of teaching experience (if available)

- Degree certificatess

- A successfully completed master's degree or equivalent in Computer Science, Business Informatics, or a closely related field is required, whereby knowledge of computer science is assumed to be a given
- Methodological competence in machine learning methods, to design and implement algorithms for offline and online classification
- Professional competence in designing and conducting EEG studies
- Methodological competence in analyzing brain signals, such as signal processing methods for investigating EEG features
- Didactic competence, teaching experience / experience of working with e-learning
- High ability to express yourself both orally and in writing
- Excellent command of written and spoken English
- Basic experience in research methods and academic writing
- IT user skills
- Ability to work in a team
- Knowledge of university processes and structures
Desirable qualifications are

- Methodological competence in developing and implementing Riemannian geometry-based transfer learning models

- Experience with Virtual Reality (VR), including the design and implementation of rehabilitation tasks

- Practical competence in designing and building an EEG-based BCI that combines post-stroke motor rehabilitation and neurofeedback in VR

- Experience with working with stroke patients

- Experience abroad

Application documents

- Letter of Motivation including ideas for a prospective doctoral project proposal

- Curriculum vitae

- List of publications, evidence of teaching experience (if available)

- Degree certificatess

www.univie.ac.at

- A successfully completed master's degree or equivalent in Computer Science, Business Informatics, or a closely related field is required, whereby knowledge of computer science is assumed to be a given
- Methodological competence in machine learning methods, to design and implement algorithms for offline and online classification
- Professional competence in designing and conducting EEG studies
- Methodological competence in analyzing brain signals, such as signal processing methods for investigating EEG features
- Didactic competence, teaching experience / experience of working with e-learning
- High ability to express yourself both orally and in writing
- Excellent command of written and spoken English
- Basic experience in research methods and academic writing
- IT user skills
- Ability to work in a team
- Knowledge of university processes and structures

- A successfully completed master's degree or equivalent in Computer Science, Business Informatics, or a closely related field is required, whereby knowledge of computer science is assumed to be a given
- Methodological competence in machine learning methods, to design and implement algorithms for offline and online classification
- Professional competence in designing and conducting EEG studies
- Methodological competence in analyzing brain signals, such as signal processing methods for investigating EEG features
- Didactic competence, teaching experience / experience of working with e-learning
- High ability to express yourself both orally and in writing
- Excellent command of written and spoken English
- Basic experience in research methods and academic writing
- IT user skills
- Ability to work in a team
- Knowledge of university processes and structures

The University of Vienna was founded in 1365 and is the oldest university in the German-speaking world and one of the largest in Central Europe. At present, about 88,000 students are enrolled in 180 courses at the University of Vienna. The University of Vienna is also the largest teaching and research institution in Austria with 8,900 employees, 6,700 of whom are scientists and academics.