Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
proposals to advance their scientific career. More information about the Department of Applied Physiology and Kinesiology can be found here: http://hhp.ufl.edu/about/departments/apk/ Expected Salary
-
Job Purpose To make a leading contribution to advanced EEG analysis of the effect of Neurofeedback for Nociplastic Pain in Rheumatoid Arthritis, working with Professor Aleksandra Vuckovic and
-
vectors studies, including vector design and cloning. RNA sequencing. Proteomics/transcriptomics and bioinformatics. PLA. Immunohistochemistry on brain sections. Surgery to implant electrodes and EEG
-
Successful candidates should have knowledge of cognitive neuroscience, Computational neuroscience, including but not limited to deep learning, Neuroimaging (MRI, EEG, MEG, ultrasound or optical imaging
-
, assessing language background and exposure/use patterns as well as neural and cognitive outcomes at baseline (via rs-EEG, as well as tasks measuring working memory and attentional control and their neural
-
-level structural and functional magnetic resonance imaging (MRI), transcranial magnetic stimulation (TMS), and human electrophysiology (EEG) data acquisition and analysis using standard and advanced
-
cognitive tasks Biomarker data collection including biospecimen samples (e.g., saliva) Collect EEG data Manage study databases, such as REDCap Report to funding bodies Assist in Institutional Review Board
-
monitoring methodologies Developing surgical EEG-lead implantation skills Conducting data management, statistical analysis, and principles of scientific study design Gaining knowledge of animal anatomy and
-
: Identification of EEG and fMRI biomarkers with potential predictive value for psychedelics’ antidepressant effects (National Institute of Mental Health (NIMH), Czechia) DC4: Mapping the subjective phenomenology
-
multiple subjects (e.g., Human Activity Recognition and EEG signal datasets). Additionally, experiments may be performed on well-known image classification datasets to further evaluate the proposed approach