Purdue University | Gilbreth Postdoctoral Fellowship

Project Description

This project will establish a systems-level framework for alcohol use disorder (AUD) by moving beyond conventional functional connectivity to disentangle neural-network organization, systemic/cerebrovascular physiology, and their interaction in resting-state fMRI. Leveraging T1-weighted and resting-state datasets, the fellow will use Dr. Tong’s expertise in systemic low-frequency BOLD physiology and lag-aware modeling to extract subject-specific hemodynamic components, including oscillation amplitudes, delay maps, propagation gradients, and physiological contributions. In parallel, Dr. Goñi’s expertise in functional connectomics, network science, geometry, dynamic connectivity, and dimensional AUD phenotyping will guide construction of conventional and physiologically corrected connectomes. Each participant will therefore be represented by two layers—a neural functional network and a hemodynamic propagation network—whose cross-layer coupling will be quantified through geometry-aware distances, gradient and community alignment, spectral similarity, and spatial null models. Associations with alcohol consumption, AUD-related problems, family history, impulsivity, age, sex, and education will test whether observed abnormalities are predominantly neural, predominantly vascular, or reflect pathological coupling or decoupling. The most transformative outcome would be identification of reduced neural–hemodynamic alignment despite preserved global physiology and connectivity strength, revealing a previously invisible AUD phenotype. This integrative, recruitment-free strategy is feasible, mechanistically informative, and readily generalizable to aging, neurodegeneration, pain, and other disorders.

Postdoc Qualifications

• PhD in biomedical engineering, neuroscience, computational neuroscience, applied mathematics, computer science, statistics, or a closely related field.
• Strong experience analyzing resting-state fMRI data, including preprocessing, quality control, nuisance regression, and functional-connectivity estimation.
• Knowledge of BOLD physiology, cerebrovascular effects, systemic low-frequency oscillations, or physiological-noise modeling.
• Experience with lag-aware analysis, time-delay estimation, signal propagation, or related time-series methods.
• Expertise in network neuroscience, including connectome construction, graph measures, community detection, gradients, and spectral methods.
• Familiarity with dynamic functional connectivity and multilayer or multimodal network analysis.
• Strong quantitative background in statistical modeling, multivariate analysis, dimensional phenotyping, and correction for multiple comparisons.
• Experience using spatial null models and accounting for cortical geometry and spatial autocorrelation.
• Proficiency in Python, MATLAB, R, or comparable scientific-computing environments.
• Ability to develop reproducible analytical pipelines for large neuroimaging datasets.
• Experience with tools such as FSL, AFNI, SPM, FreeSurfer, fMRIPrep, Nilearn, or comparable software.
• Familiarity with Riemannian geometry, covariance-based representations, or geometry-aware distances would be advantageous.
• Experience with substance-use, psychiatric, or behavioral phenotyping—particularly alcohol use disorder—is desirable.
• Ability to integrate physiological, imaging, demographic, and behavioral data.
• Strong scientific writing and visualization skills, with evidence of peer-reviewed publications.
• Commitment to open science, including documented code, version control, data provenance, and reproducible reporting.
• Capacity to work independently while collaborating effectively across neuroimaging, physiology, network-science, and clinical teams.
• Interest in translating methodological developments beyond AUD to aging, neurodegeneration, pain, and other health conditions.

Key Dates for 2026

• July 29, 2026: call to engineering faculty to post research topics on the LGPF website
• September 14, 2026: website with proposed topics made live to interested applicants
• October 16, 2026: deadline to receive full application packets with recommendation letters
• January 2027: the 2027-28 Lillian Gilbreth postdoc fellows announced; Fellows can start their assignments as early as February 2027

Application

Application materials include the following items:

1. Cover letter describing how the Gilbreth Fellowship will help the candidate progress toward their goals in Engineering academia.
2. Curriculum Vitae including list of publications.
3. One-page research statement that includes goals and significance of proposed interdisciplinary work and lists potential Purdue engineering faculty members who can be co-advisors.
4. A one-page essay on the candidate’s proposed broader impact through education/outreach/engagement.
5. Three letters of recommendation that include detailed assessments of (a) the candidate’s qualifications and (b) potential for success in academia through scholarly research and broader impact through research/education/engagement. Letters from potential Purdue co-advisors will not be accepted.

Applications accepted beginning September 14, 2026. Click here to apply. Applications will be due by October 16, 2026.

Create an account by clicking on the Register button at the bottom of the Log In page. Here you will input your name and email address and click on Create Account. You will be asked to confirm your account via an email notification sent to the email address entered. Use this email address and established password to access the site. Please note that email domains affiliated with institutions, e.g., edu, are required.

If you have questions about the application process, please email us at coeoaa@purdue.edu.

Application and more details: https://engineering.purdue.edu/Engr/Research/GilbrethFellowships/ResearchProposals/2025-26/beyond-functional-connectivity-disentangling-neural-and-hemodynamic-network-dysfunction-in-alcohol-use-disorder