Scholarship: Fully-funded
Degree: Ph.D
Nationality: International Students
Location: USA
Application deadlines: Open
Scholarship Description:
The AI2D Center at the University of Pennsylvania has competitive computing resources and medical data. The Department of Pathology is one of two hospital-based departments at the Perelman School of Medicine, performing an average of 10 million diagnostic tests annually.
This project leverages the University of Pennsylvania Medical Academic Computing Services (PMACS) high-performance computing (HPC) system and the AI2D CUBIC cluster, providing robust computational infrastructure:
The PMACS HPC system includes 76 computing nodes with 6,080 CPU cores, 4.2 PB of storage, deep learning nodes equipped with NVIDIA Tesla P100 GPUs, and 8 NVIDIA H100 GPUs. It also offers a Dell R940 server with 1.5 TB of RAM for high-memory tasks.
The AI2D CUBIC cluster is dedicated to biomedical image analysis, featuring 169 servers with 11,368 CPU cores, 70 TB of RAM, and 128 NVIDIA A100 and V100 GPUs.
Additionally, the University of Pennsylvania’s Test Development Laboratory (TDL) is equipped with state-of-the-art slide scanning technology for digitizing pathology slides. The lab has two FDA-approved Hamamatsu NanoZoomer S360 scanners that can process up to 360 slides simultaneously, providing high-resolution scans for AI training and diagnostic validation. The hospital’s surgical pathology department also has an additional NanoZoomer S360, further enhancing research capabilities.
Available Subjects:
- Develop and apply cutting-edge AI for Pathology models, focusing on (but not limited to) Neuropathology Whole Slide Images (WSI) and omics data analysis.
- Publish and present research findings in high-impact scientific journals and conferences, exploring opportunities for technology commercialization.
- Collaborate in an interdisciplinary team environment, fostering open communication and mutual respect to ensure efficient teamwork.
Eligibility criteria:
- Recent or upcoming Ph.D. graduates with research backgrounds in fields such as Computer Science, Electrical Engineering, Bioinformatics, Biostatistics, Computational Biology, Computational Pathology, Genomics, Genetics, Statistics, or related areas are encouraged to apply.
- Candidates should have experience in Machine Learning, Deep Learning, Software Engineering, and Biomedical Data.
- Proficiency in programming languages like Python is required.
- Preference will be given to candidates familiar with Pathology Image Analysis and Radiology AI.
- Strong communication skills and the ability to collaborate in an interdisciplinary team environment are essential.
Application Procedure:
Interested applicants should send the following materials to Dr. MacLean Nasrallah at [email protected] and Dr. Zhi Huang at [email protected].
Please use the email subject:
PostdocapplicationatPennAI2DPostdoc application at Penn AI2DPostdocapplicationatPennAI2D – Firstname Lastname (replace “Firstname Lastname” with your actual name).
