Postdoctoral Fellow in Machine Learning in the Dept. of Psychiatry, Baylor College of Medicine & Electrical & Computer Engineering, Rice University

The Papageorgiou/Investigational Targeted Brain Neurotherapeutics Laboratory focuses on the mechanisms involved in neuro-rehabilitation as a function of non-invasive brain stimulation strategies. Our studies focus on a variety of innovative approaches, including neurofeedback in targeted and individualized regions of interest based on the patient's location and spatial extent of the lesion, optimization of rt-fMRI nFb methods with the long-term goal of central and peripheral nervous system injury neuro-rehabilitation, as well as using highly quantitative methods, such as population receptive field, multivariate and univariate analyses. More specifically, our laboratory studies: (i) the mechanisms that guide plasticity following insult to the brain by using structural, volumetric, diffusion tensor imaging and functional connectivity measures; (ii)  functional plasticity/reorganization of the brain by applying a targeted and individualized real-time fMRI neurofeedback method; and (iii) optimized neurofeedback approaches via computational modelling.

The Papageorgiou Lab is seeking a highly motivated post-doctoral fellow to conduct innovative computational modeling on cortical repair and neuromodulation. The goal of this project is to uncover the underlying mechanisms of cortical repair using, fMRI and real-time functional MRI neurofeedback  combined with EEG data acquisition in the long-term. To achieve this, the postdoctoral fellow will use computational modeling and machine learning. The project promises to be challenging, but also presents a highly exciting opportunity to gain an understanding of innovative neurofeedback methods applied to health and neurological disease (cortical blindness, chronic pain and other neurological disorders. The applicant MUST have the passion and motivation to pursue innovative scientific research with a flexible work schedule.

Essential Duties

Perform machine learning and deep learning data analyses on neuroimaging data already collected and available for analyses.

Prepare and conduct technical presentations.

Document research, and write-up papers in peer-reviewed journals.

Required Qualifications

Prerequisite is a Ph.D. in a relevant field: Applied Math; Neuroscience; Electrical Engineering; Bio-engineering/Biomedical Engineering; Applied Physics.

Demonstrated advanced analytical and experimental skills.

Strong expertise in signal processing.

Demonstrated written and verbal communication skills to author technical and scientific reports, publications, and invited papers, and deliver scientific presentations.

Good interpersonal communication skills necessary to collaborate effectively in a team environment and be capable of independent original work.

Preferred Qualifications on any of the following

Preference will be given to applicants with a PhD in Applied Math

Machine Learning

Signal processing

Experience in fMRI methods and analyses: AFNI experience will be strongly preferred

Apply: Please send a pdf or word CV, 3-4 references, and cover letter to Dr. Dorina Papageorgiou at [log in to unmask]

Compensation commensurate with experience.

Baylor College of Medicine is an Equal Opportunity /Affirmative Action/Equal Access Employer