Texas A&M University
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Postdoctoral Research Associate Texas A&M University in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Deadline
Dec 1, 2026
Country
United States
University
Texas A&M University
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About this position
Postdoctoral Research Associate in the Papageorgiou Laboratory at Texas A&M School of Engineering Medicine
This postdoctoral role is centered on advancing an existing AI-guided, real-time fMRI neuromodulation platform being developed in the Papageorgiou Laboratory at Texas A&M University’s School of Engineering Medicine (EnMed). The lab’s research aims to support precision neurorehabilitation by translating each patient’s anatomical and functional brain network into a closed-loop intervention that adapts in real time.
The successful candidate will work on the computational and engineering backbone of the platform, with responsibilities spanning software interfaces, real-time data processing, visualization, hardware-software integration, system reliability, and performance monitoring. The project combines electrical engineering, neuroengineering, computational neuroscience, and biomedical imaging in a multidisciplinary setting.
Day-to-day work includes maintaining and improving the closed-loop pipeline for fMRI-based neuromodulation, developing participant-facing displays, handling synchronization and latency issues, and building logging and diagnostic tools to detect data loss, timing drift, and processing failures. The role also involves applying computational methods such as signal processing, machine learning, dimensionality reduction, classification, state-space and hidden Markov modeling, and network-level analysis.
The position is embedded in a collaborative environment that brings together clinicians, neuroscientists, engineers, imaging scientists, and computational researchers. The postdoctoral researcher may also support MRI sessions with human participants, contribute to manuscripts and presentations, and assist with documentation and technical progress reports.
This is a research-intensive role focused on real-time neurotechnology development and translation within Texas A&M Health.
Funding details
Available
What's required
Ph.D. or equivalent doctoral degree in Electrical Engineering, Computer Engineering, Biomedical Engineering, Computer Science, Medical Physics, or a closely related quantitative discipline is required. The successful candidate must complete human-subjects research (CITI) and MRI safety training within 30 days of hire. Preferred qualifications include three years of experience with real-time systems, hardware-software interfaces, and MRI/fMRI acquisition or analysis; experience developing, evaluating, or troubleshooting computational or engineering systems through doctoral research or equivalent advanced training; experience with scientific programming and quantitative data analysis demonstrated by first-author publications or equivalent work products; and a record of self-motivation, independent problem-solving, and sustained commitment to complex projects. Desired knowledge includes MRI neurofeedback, brain-computer interfaces, closed-loop biomedical systems, relevant neuroimaging software, Siemens real-time data export, asynchronous/multithreaded/multiprocessing architectures, real-time signal processing, network communication, hardware triggering, GPU or parallel computing, machine learning, functional connectivity/network analysis, and research software for human fMRI experiments. Applicants should have advanced programming skills in Python, MATLAB, C/C++, or related languages, strong software engineering practices, Linux/Unix and shell scripting knowledge, strong quantitative reasoning and troubleshooting skills, ability to translate scientific/engineering requirements into computational solutions, ability to work independently and collaboratively, knowledge of data privacy and secure handling of human-subjects’ data, and attention to detail.
How to apply
Apply through the Texas A&M Workday application page. Submit either all required application data or a complete resume; a LinkedIn profile may also be used to pre-populate the application. Ensure your materials are complete before submission.
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Review drafts and confirm requirements with the university.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.