Postdoctoral Research Associate (AI/fMRI)

Location: Houston, Texas, United States of America

Company

Texas A&M University’s School of Engineering Medicine conducts academic health research and develops transformative education and team-based healthcare solutions.

What you will do

• Optimize and maintain a real-time closed-loop fMRI neuromodulation platform for precision neurorehabilitation.
• Develop software interfaces, participant-facing graphical and user interfaces, real-time data pipelines, visualization, logging, and closed-loop output generation.
• Integrate software with imaging systems, workstations, networked data streams, displays, and experimental hardware while managing timing, synchronization, latency, queueing, and overruns.
• Implement diagnostics, structured logging, validation, and regression testing to identify missed data, dropped samples, timing drift, and processing failures.
• Apply signal processing, machine learning, dimensionality reduction, classification, state-space and hidden Markov modeling, and network-level analysis.
• Support MRI sessions with human participants and collaborate on research, mentoring, manuscripts, presentations, documentation, and grant-related reporting.

Requirements

• Ph.D. or equivalent doctoral degree in Electrical Engineering, Computer Engineering, Biomedical Engineering, Computer Science, Medical Physics, or a closely related quantitative discipline.
• Complete human-subjects research (CITI) and MRI safety training within 30 days of hire.
• Advanced programming skills in Python, MATLAB, C/C++, or related languages.
• Strong knowledge of software engineering practices, including debugging, testing, version control, documentation, configuration management, and reproducibility.
• Understanding of real-time systems concepts, including latency, synchronization, timing drift, buffering, dropped data, and failure recovery.
• Knowledge of Linux/Unix environments and shell scripting, with strong quantitative reasoning, troubleshooting, and problem-solving skills.
• Ability to translate scientific and engineering requirements into computational solutions and work independently within a multidisciplinary team.
• Knowledge of data privacy and secure handling of human-subjects’ data, with attention to detail.

Nice to have

• Three years of experience with real-time systems, hardware-software interfaces, or MRI/fMRI acquisition or analysis.
• Experience developing, evaluating, or troubleshooting computational or engineering systems through doctoral research or equivalent advanced training.
• Scientific programming and quantitative data analysis demonstrated through first-author publications or equivalent work products.
• Self-motivation, independent problem-solving, and sustained commitment to complex projects.
• Knowledge of MRI neurofeedback, brain-computer interfaces, closed-loop biomedical systems, AFNI, FSL, SPM, Turbo-BrainVoyager, Siemens real-time data export, asynchronous or multithreaded architectures, real-time signal processing, network or inter-process communication, hardware triggering, GPU or parallel computing, machine learning, functional connectivity, network analysis, or research software for human fMRI experiments.

Culture & Benefits

• Work in a multidisciplinary academic research environment emphasizing respect, excellence, leadership, loyalty, integrity, and selfless service.
• Medical, prescription drug, dental, vision, life, AD&D, flexible spending account, and long-term disability insurance, with Texas A&M contributing to employee health and basic life premiums.
• 12–15 annual paid holidays, monthly paid sick leave, and at least eight hours of paid vacation each month.
• Automatic enrollment in the Teacher Retirement System of Texas.
• Free exercise programs, release time, LinkedIn Learning, professional development support, conference and workshop assistance, educational release time, and tuition assistance.

Hiring process

• Submit complete job application data or attach a complete resume; an incomplete submission may be rejected. A resume or LinkedIn profile may be used to pre-populate the application.
• All positions are security-sensitive and require a criminal history investigation and verification of credentials and other required information.