at Umeå center for Functional Brain Imaging (UFBI) and related institutes/labs.
Postdoctoral Fellowship (2 years) in research project on biomarkers for cognitive aging at the Department of Psychology
The Department of Psychology at Umeå University offers a postdoctoral fellowship within the project "Longitudinal Profiling of the Aging Plasma Proteome to Identify Risk Profiles for Neurocognitive Aging." The fellowship is for two years, starting on August 15, 2025, or as agreed.
The research group led by Sara Pudas is looking for a motivated postdoctoral fellow for a two-year fellowship in blood-based biomarkers for neurocognitive aging. The project is based on existing data from a prominent longitudinal aging study, the Betula Project, as well as the UK Biobank. The candidate will handle, process, and statistically analyze large amounts of data on blood markers, cognitive tests, and health and lifestyle factors. If interested, the postdoc candidate can also choose to be part of the research environment at the Umeå Center for Functional Brain Imaging. The project will be conducted on-site in Umeå, and remote work is not possible.
Two Postdoctoral scholarship positions in Machine Learning with focus on Brain analysis at Luleå University of Technology.
The department of Computer Science, Electrical and Space Engineering at Luleå University of Technology (LTU) is offering two scholarships for a Postdoctoral Fellow to carry out research with the Machine learning group in the area of Brain data analysis for inner speech decoding. Machine learning focuses on computational methods by which computer systems use data to improve their own performance, understanding, and to make accurate predictions and has a close connection to applications.
Project description The postdoctoral positions are part of a funded 2-year project supported by Kempestiftelsen, focused on decoding inner speech using multimodal EEG and fMRI data. The successful candidates will work with two unique bimodal datasets, enabling investigation into both the temporal and spatial aspects of inner speech. Using cutting-edge machine learning techniques, including deep neural networks and graph-based models, the project will explore how to optimally fuse EEG and fMRI data and develop accurate inner speech decoding frameworks. This interdisciplinary project combines cognitive neuroscience and AI. It is hosted at Luleå University of Technology (LTU) within the Brain Analysis of the Machine Learning Group and benefits from strong institutional support, access to MRI-compatible EEG hardware, and high-performance GPU infrastructure.
