What are the responsibilities and job description for the Post-Doctoral Research Fellow position at MICCAI Society?
Wednesday 4th March 2026
Contact Email for the Job Positing dk1362@rwjms.rutgers.edu
Organization Rutgers Robert Wood Johnson Medical School
Location New Brunswick
Title Post-Doctoral Research Fellow
URL https://jobs.rutgers.edu/postings/270151
Closing date Mar 31, 2026
Description Description
The AiCCESS Lab at Rutgers Robert Wood Johnson Medical School invites applications for a fully funded Postdoctoral Fellow position at the intersection of computer vision, multimodal deep learning, and clinical surgery.
Our lab focuses on the development and real-world deployment of surgical AI systems, with a current emphasis on predicting and preventing surgical site infections (SSIs) — a leading cause of postoperative morbidity affecting millions of patients annually. Since our founding in late 2024, the lab has secured over $450K in active funding and established an international consortium of surgical AI researchers with collaborations at Johns Hopkins University, Stanford University, and the University of Florida.
Project Focus
The Selected Fellow Will Lead The Design, Development, And Validation Of Vision Transformer-based Architectures Trained On Rich Multimodal Surgical Datasets, Including
Intraoperative surgical video
Postoperative wound imaging
Electronic health record (EHR) data
Real-time intraoperative sensor streams (thermal imaging, particulate matter)
The core objective is to build robust, clinically deployable AI pipelines capable of predicting SSI risk intraoperatively and postoperatively — moving beyond retrospective analysis toward real-time surgical intelligence.
Responsibilities
Architect and optimize vision transformer and multimodal fusion models for SSI prediction
Develop end-to-end ML pipelines from raw multimodal data to validated clinical predictions
Collaborate with surgeons, data scientists, and implementation scientists across institutions
Lead and contribute to peer-reviewed manuscript preparation and conference submissions
Present research at national and international venues including MICCAI, ACS, and AAST
Contribute to grant reporting and future funding applications
Required Qualifications
PhD in Computer Science or Computational Biology
Demonstrated Expertise In One Or More Of The Following
Vision transformers (ViT, Swin, etc.)
Multimodal deep learning
Surgical video analysis or medical image computing
Strong Python programming skills with PyTorch and/or TensorFlow
Evidence of peer-reviewed publication or preprint output
Preferred Qualifications
Experience with clinical or biomedical datasets (EHR, DICOM, wearable/sensor data)
Familiarity with cloud infrastructure (AWS) and clinical data platforms (REDCap, InfluxDB)
Prior work in surgical AI, medical image segmentation, or intraoperative decision support
Experience in translational or implementation science contexts
Infrastructure & Resources
Fellows Will Have Access To
Proprietary multimodal surgical datasets collected across active clinical sites
Cloud computing infrastructure (AWS)
Clinical data management platforms (REDCap, InfluxDB)
Interdisciplinary mentorship spanning surgery, computer science, and implementation science
Active research network across Johns Hopkins, Stanford, and the University of Florida
Compensation & Benefits
Fully funded, competitively salaried position per NIH postdoctoral pay scale
Renewal eligible for second year based on performance
Travel support for conference presentations
Hybrid work flexibility.
How to Apply
Contact Email for the Job Positing dk1362@rwjms.rutgers.edu
Organization Rutgers Robert Wood Johnson Medical School
Location New Brunswick
Title Post-Doctoral Research Fellow
URL https://jobs.rutgers.edu/postings/270151
Closing date Mar 31, 2026
Description Description
The AiCCESS Lab at Rutgers Robert Wood Johnson Medical School invites applications for a fully funded Postdoctoral Fellow position at the intersection of computer vision, multimodal deep learning, and clinical surgery.
Our lab focuses on the development and real-world deployment of surgical AI systems, with a current emphasis on predicting and preventing surgical site infections (SSIs) — a leading cause of postoperative morbidity affecting millions of patients annually. Since our founding in late 2024, the lab has secured over $450K in active funding and established an international consortium of surgical AI researchers with collaborations at Johns Hopkins University, Stanford University, and the University of Florida.
Project Focus
The Selected Fellow Will Lead The Design, Development, And Validation Of Vision Transformer-based Architectures Trained On Rich Multimodal Surgical Datasets, Including
Intraoperative surgical video
Postoperative wound imaging
Electronic health record (EHR) data
Real-time intraoperative sensor streams (thermal imaging, particulate matter)
The core objective is to build robust, clinically deployable AI pipelines capable of predicting SSI risk intraoperatively and postoperatively — moving beyond retrospective analysis toward real-time surgical intelligence.
Responsibilities
Architect and optimize vision transformer and multimodal fusion models for SSI prediction
Develop end-to-end ML pipelines from raw multimodal data to validated clinical predictions
Collaborate with surgeons, data scientists, and implementation scientists across institutions
Lead and contribute to peer-reviewed manuscript preparation and conference submissions
Present research at national and international venues including MICCAI, ACS, and AAST
Contribute to grant reporting and future funding applications
Required Qualifications
PhD in Computer Science or Computational Biology
Demonstrated Expertise In One Or More Of The Following
Vision transformers (ViT, Swin, etc.)
Multimodal deep learning
Surgical video analysis or medical image computing
Strong Python programming skills with PyTorch and/or TensorFlow
Evidence of peer-reviewed publication or preprint output
Preferred Qualifications
Experience with clinical or biomedical datasets (EHR, DICOM, wearable/sensor data)
Familiarity with cloud infrastructure (AWS) and clinical data platforms (REDCap, InfluxDB)
Prior work in surgical AI, medical image segmentation, or intraoperative decision support
Experience in translational or implementation science contexts
Infrastructure & Resources
Fellows Will Have Access To
Proprietary multimodal surgical datasets collected across active clinical sites
Cloud computing infrastructure (AWS)
Clinical data management platforms (REDCap, InfluxDB)
Interdisciplinary mentorship spanning surgery, computer science, and implementation science
Active research network across Johns Hopkins, Stanford, and the University of Florida
Compensation & Benefits
Fully funded, competitively salaried position per NIH postdoctoral pay scale
Renewal eligible for second year based on performance
Travel support for conference presentations
Hybrid work flexibility.
How to Apply
Salary : $450,000