Postdoctoral fellow / staff scientist / engineer AI-based tools development for low-field brain MRI processing
Thursday 17th April 2025
Contact Email for the Job Positing nlepore@chla.usc.edu
Organization Children's Hospital Los Angeles
Location Los Angeles, California
Title Postdoctoral fellow / staff scientist / engineer AI-based tools development for low-field brain MRI processing
URL https://www.chla.org/
Closing date May 17, 2025
Description We are looking for one PhD level scientist (postdoctoral fellow / staff scientist / engineer) who will design novel AI-based methods to process and analyze a large, multisite database of ultra-low magnetic field (0.064T) pediatric brain MRI (LF-MRI). They will work with a team of researchers at Children's Hospital Los Angeles and with our collaborators around the world who are gathering, processing, and analyzing the dataset. The focus of this work will be more specifically on designing new tools for the segmentation of LF-MRI and on organizing a segmentation hackathon. The position is temporary (from now until Dec 31, 2025), but may be extended based on funding, and can be in person or remote.
Required Skills and Qualifications
- Strong proficiency in Python for scientific computing and deep learning application development.
- Hands-on experience with PyTorch, including:
- Training, and evaluating convolutional neural networks (CNNs) based models
- Writing custom loss functions, data loaders, and model architectures
- Understanding of medical image analysis, especially MRI data processing:
- Experience with working with 2D/3D U-Net, attention-based networks, or transformer-based models and running current state-of-the-art segmentation methods.
- Proficient in using libraries such as: nibabel, SimpleITK, scikit-image, NumPy, Pandas, Matplotlib
- Experience working with GPU acceleration, batch processing, and optimizing training performance
- Familiarity with version control (Git) and collaborative development workflows
Preferred skills
- Experience with neuroimaging formats (e.g., NIfTI, DICOM)
- Knowledge of data augmentation techniques for 3D medical imaging
- Background in neuroscience or biomedical imaging
- Experience developing new models specifically for segmentation for subcortical pediatric brain MR images.