Senior Scientist at the Department of Cognition, Emotion, and Methods in Psychology

The University of Vienna (20 faculties and centres, 184 fields of study, approx. 10.400 members of staff, about 90.000 students) seeks to fill the position as soon as possible of a


Reference number: 13936

The Faculty of Psychology consists of the Dean’s office and the studies service center, as well as • Department of Cognition, Emotion, and Methods , • Department of Occupational, Economic and Social Psychology, • the Department of Applied Psychology: Work, Education and Economy, • Department of Developmental and Educational Psychology • the Outpatient Unit for Research, Teaching and Practice. Currently, nearly 3,500 students of Psychology are enrolled in Bachelor's, Master's and PhD programmes, attending nearly 200 courses per semester, which are taught and supervised by 220 teachers.





Extent of Employment: 40.0 hours/week
Job grading in accordance with collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) with relevant work experience determining the assignment to a particular salary grade.

Job Description:
Applicants should have a strong interest in machine learning methods applied to psychological and clinical data. This includes excellent knowledgde of interpretable machine learning tools and experience in data management (DSGVO). They should also be experienced in teaching machine learning methods to psychology students through lectures and hands-on practical training, including supervision of psychology Theses and internship projects in this field. Further, applicants should also have a solid background in neurotechnologies, including brain-computer interfaces based on real-time neuroimaging and physiological signals. This includes online signal processing of fMRI, EEG, simultaneous EEG-fMRI, EMG, EOG, EDA, ECG, HR, respiration as well as eye tracking data, and their multimodal integration via LabStreamingLayer. Experience with joint Tech-University research projects and grants as well as with SCRUM workflow management is an advantage. Applicants should also demonstrate successful contributions of machine learning and neurotechnology methods to support third-party funding initiatives.
Applicants should have one of the following profiles: A degree in Biomedical Engineering and/or Computer Science, with solid experience in machine learning and neurotechnologies. Applicants should send their CV, a brief statement of research interests, and the names and contact information for 2 references. Participation in teaching and independent teaching of courses as defined by the collective agreement.


Profile:
Candidates must have a very good PhD in Biomedical Engineering and/or Computer Science. Furthermore, experience in teaching machine learning methods to psychology students and very good communication skills in German and English are required.

The following skills and activities are considered a plus: ability to work in a team; autonomous and proactive working style; evidence of independent research as a senior author; experience as a reviewer and editor; outreach activities; Python (e.g, scikit-learn, scikit-optimize, matplotlib, lightgbm, xgboost, shap, scipy, pandas); Matlab (e.g. SPM); experience with real-time neuroimaging and physiological signals (online signal processing, fMRI, EEG, simultaneous EEG-fMRI, EMG, EOG, EDA, ECG, HR, respiration, eye tracking); LabStreamingLayer; experience with joint Tech-University research projects and grants; SCRUM experience (e.g. certified SCRUM Master); experience in computer infrastructure administration, data storage, management and sharing; DSGVO training.


Research fields:

Main research field	Special research fields	Importance
Medical Engineering	Biomedical engineering	SHOULD

Education:

Educational institution	Educational level	Special subject	Importance
University	Electrical Engineering, Electronics	Electro- an biomedical technology	SHOULD
University	Mathematics, Computer Sciences	Informatics	SHOULD

Languages:

Language	Language level	Importance
German	Very good knowledge	MUST
English	Very good knowledge	MUST

Computer-Skills:

Type of computer skills	Specified computer skills	Importance
Programming language	Others	CAN
Basic Knowledge	Others	CAN
Basic Knowledge	Others	CAN

Applications including a letter of motivation (German or English) should be submitted via the Job Center to the University of Vienna ( http://jobcenter.univie.ac.at) no later than 05.04.2023, mentioning reference number 13936.

For further information please contact Scharnowski, Frank +43-1-4277-47120.

The University pursues a non-discriminatory employment policy and values equal opportunities, as well as diversity ( http://diversity.univie.ac.at/). The University lays special emphasis on increasing the number of women in senior and in academic positions. Given equal qualifications, preference will be given to female applicants.

Human Resources and Gender Equality of the University of Vienna
Reference number: 13936
E-Mail: jobcenter@univie.ac.at
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