Job Information
- Organisation/Company: Universitatea din Bucuresti
- Department: Centru de Excelență pentru Schimbări Climatice și Societale
- Research Field: Philosophy » Other
- Researcher Profile: First Stage Researcher (R1)
- Positions: PhD Positions
- Application Deadline: 8 Sep 2026 - 14:00 (Europe/Bucharest)
- Country: Romania
- Type of Contract: Temporary
- Job Status: Part-time
- Hours Per Week: 20
- Offer Starting Date: 28 Sep 2026
- Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
Offer Description
The University of Bucharest is announcing a competition to fill a position for a research assistant, COR code 263306, within the project “Center of Excellence for Climate and Societal Change,” project code PN-IV-P6-6.1-CoEx-2024-0042, funded by UEFISCDI.
Employment period: fixed-term – December 31, 2027
Work schedule - 4 hours/day.
Where to apply
E-mail: concursuri@hr.unibuc.ro
Requirements
Research Field: Philosophy » Other
Education Level: PhD or equivalent
Skills/Qualifications
- Ph.D. student in the field of Philosophy.
- Programming skills (e.g., Python), demonstrated through projects, publications, a portfolio, or professional experience.
- Skills in artificial intelligence / machine learning
- Skills in psychophysiological data acquisition
- Knowledge of biological signal processing: filtering, artifact detection and removal, independent component analysis (ICA), feature extraction.
- Knowledge of research methodology in the social and behavioral sciences (experimental design, data collection, and data management).
- Communication and interpersonal skills for interacting with study participants.
- Knowledge of English at a level sufficient to work with the scientific literature.
Specific Requirements
Job Responsibilities:
- Contribute to the design and implementation of experiments.
- Program and automate experimental protocols (stimulus presentation, data collection).
- Acquire psychophysiological data in the laboratory.
- Pre-process and analyze signals (filtering, artifact detection, ICA, feature extraction).
- Developing, training, and evaluating machine learning models for behavioral and physiological data.
- Managing, cleaning, and documenting datasets.
- Complying with ethical protocols and personal data protection regulations.
- Contributing to the drafting of research reports and publications.
Languages: ENGLISH — Level: Good
Languages: ROMANIAN — Level: Mother Tongue
Research Field: Philosophy » Other
Years of Research Experience: 1 - 4
Additional Information
Benefits
Topics:
- Psychophysiological data acquisition: principles of electroencephalography (EEG) and electrodermal activity (EDA) measurement; setup, acquisition systems, signal quality.
- Preprocessing of psychophysiological signals: filtering, segmentation, artifact detection and removal, independent component analysis (ICA) for EEG; decomposition of EDA into tonic and phasic components (electrodermal responses).
- Feature extraction and signal analysis.
- Programming for experimental research: Python and specialized libraries; stimulus presentation (e.g., PsychoPy); automation of data workflows.
- Machine learning / artificial intelligence for behavioral and physiological data.
- Research methodology in behavioral and environmental psychology; data management and cleaning.
- Ethical considerations in research involving human participants; protection of personal data (GDPR).
Eligibility criteria
Bibliography:
- American Psychological Association (2017). Ethical Principles of Psychologists and Code of Conduct. https://www.apa.org/ethics/code
- Boucsein, W. (2012). Electrodermal activity (2nd ed.). Springer Science + Business Media. https://doi.org/10.1007/978-1-4614-1126-0
- Brunet, D., Murray, M. M., & Michel, C. M. (2011). Spatiotemporal analysis of multichannel EEG: CARTOOL. Computational Intelligence and Neuroscience, 2011, 813870. https://doi.org/10.1155/2011/813870
- Delorme, A., & Makeig, S. (2004). EEGLAB: an open-source toolbox for the analysis of single-trial EEG dynamics, including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
- Gramfort, A. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7. https://doi.org/10.3389/fnins.2013.00267
- Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lespinasse, F., Pham, H., Schölzel, C., & Chen, S. H. A. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods, 53(4), 1689–1696. https://doi.org/10.3758/s13428-020-01516-y
- Michel, C. M., & Koenig, T. (2018). EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. NeuroImage, 180(Pt B), 577–593.
- World Medical Association (2013). Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA, 310(20), 2191–2194.
Selection process
Competition stages:
- Review of application materials
- Written exam
- Interview.
Additional comments
If you have any questions, please feel free to contact us by email at Prof. Iuliana Armaș, Ph.D., project manager: iulia_armas@geo.unibuc.ro
Website for additional job details: https://unibuc.ro/despre-ub/cariere/posturi-cercetare/#1544693602256-6d9237ad-7fe3
Work Location(s)
Number of offers available: 1
Company/Institute: Universitatea din Bucuresti - Rectorat
Country: Romania
State/Province: Bucharest
City: Sector 5
Postal Code: 050663
Street: Sos Panduri 90
Contact
State/Province: Bucharest
City: Bucharest
Website: http://www.unibuc.ro/n/despre/
Street: Soseaua Panduri, no. 90
Postal Code: 050663
E-Mail: office@g.unibuc.ro
Phone: 021.305.97.30
Voici une liste de prévisions...
Vous devez vous connecter pour voir la description complète de l'emploi et postuler.
Gérer / Mettre à niveau cet emploi vers une liste complète d'emploi.
Trouvez votre meilleure opportunité
leur dire AcademicJobs.com Envoyez vous !