Postdoctoral position Full-time

Postdoctoral Position

Lund University was founded in 1666 and is repeatedly ranked among the world's top universities. The University has around 46,000 students and 8,500 staff based in Lund, Helsingborg, and Malmö. We are united in our efforts to understand, explain, and improve our world and the human condition.

The position is based at the Department of Psychology at Lund University, within a research environment focused on the cognitive neuroscience of memory. The research group combines electrophysiological methods with high temporal resolution, particularly EEG, with eye tracking and advanced computational approaches to investigate how information is encoded, integrated, and retrieved from memory.

The Department of Psychology conducts research and education within a broad psychological field, with central research areas including work and organizational psychology, clinical psychology, cognition, neuropsychology, personality and social psychology, and developmental psychology. The department has a strong research environment and a long-standing internationalized educational environment.

Lund University is a public authority, which means that employees receive special benefits, generous annual leave, and a favorable occupational pension.

The successful candidate will contribute to a project investigating memory for naturalistic experience. The project aims to identify neural signatures of memory encoding, organization, and retrieval during naturalistic cognition, with a particular focus on time-resolved decoding and the generalization of cognitive states.

A central aim of the project is to understand how people transform rapidly changing sensory input into coherent narrative flow and stable thematic representations. The work will involve designing and implementing pipelines that integrate preprocessing, feature extraction, and model training, with a focus on robustness, generalizability, and interpretability.

Work duties include, among other things:

• Designing, conducting, and analyzing EEG experiments related to memory and naturalistic cognition.
• Developing and critically evaluating machine learning pipelines for multilevel decoding of EEG data.
• Analyzing time-resolved neural data, including temporal generalization and cross-condition decoding.
• Implementing advanced EEG preprocessing pipelines, including artifact correction and ICA.
• Contributing to the development of real-time or near-real-time decoding pipelines, including online preprocessing, feature extraction, and model updating.
• Evaluating trade-offs between latency, accuracy, and generalizability in online classification.
• Conducting statistically rigorous analyses, including permutation testing and mixed-effects modeling.
• Contributing to publications, presentations, and collaborative research activities within the project.

A person is eligible for employment as a postdoctoral researcher if they have completed a PhD, or an international degree deemed equivalent to a PhD, in psychology, cognitive neuroscience, neuroscience, computer science, biomedical engineering, or another closely related field relevant to the position.

Priority should be given to applicants who have completed their PhD no more than three years before the application deadline. Applicants who completed their PhD earlier may also be considered if there are special reasons, such as leave due to illness, parental leave, clinical service, elected positions in trade unions, or other similar circumstances.

The doctoral degree must be completed no later than the date on which the employment decision is made.

We are looking for a candidate with documented advanced methodological expertise in EEG analysis and machine learning. The candidate should have a strong track record in applying and critically evaluating classification or decoding methods for neural or other time-series data, including single-trial analyses and rigorous model validation.

The ideal candidate will combine solid knowledge of electrophysiological signal processing with strong programming skills in Python and/or MATLAB, for example using MNE-Python, scikit-learn, FieldTrip, or EEGLAB. Strong statistical skills relevant to neuroimaging data are also required.

Experience extending beyond traditional ERP-based analyses toward multivariate and time-resolved approaches is required. Experience with time-resolved EEG decoding, such as temporal generalization, as well as a fundamental understanding of feature construction, dimensionality reduction, and model interpretability for neural data, is particularly desirable.

Knowledge of multivariate frameworks, such as MVPA or RSA, and/or modern methods for time-series modeling, including deep learning where relevant, is considered an advantage. Experience with real-time data processing, online classification, or low-latency machine learning pipelines is highly desirable, as is experience in developing reproducible and well-documented analysis workflows.

Applicants must also demonstrate the ability to conduct independent research at a high scientific level, documented through relevant peer-reviewed publications.

Particular emphasis will be placed on:

• Deep methodological expertise in EEG and neural data modeling.
• Documented ability to develop and evaluate decoding and classification methods.
• Scientific quality, originality, and relevance of previous research.
• Ability to work independently and contribute to a collaborative research environment.
• Experience with reproducible research practices and well-documented computational workflows.

This is a full-time fixed-term employment as a postdoctoral researcher for three years, with a starting date of 1 July 2026 or by agreement. The position is linked to an international research project. A significant part of the employment period will be spent in an international research environment. The detailed planning of the stay abroad will be made in dialogue with the department.