The University of Texas M. D. Anderson Cancer Center is looking to solve the business and technical problem of investigating structural and functional brain changes in cancer populations by identifying imaging biomarkers and brain-behavior relationships that inform clinical outcomes, therapeutic response, and long-term survivorship.
Requirements
- advanced training in neuroscience and computational biology
- demonstrated experience developing reproducible pipelines
- integrating structural, functional and diffusion MRI data with clinical and behavioral variables
- performing quantitative image analysis
- applying machine learning
- statistical modeling
- software tool development
Responsibilities
- Design, implement, and optimize novel algorithms for the quantitative analysis of medical imaging data such as MRI, PET, and CT scans.
- Employ advanced techniques including signal processing, statistical modeling, and AI/ML to extract imaging biomarkers associated with tumor biology, disease progression, and therapeutic response.
- Analyze and integrate high-dimensional datasets from various sources including radiologic imaging, genomics, transcriptomics, pathology, and clinical metadata.
- Develop standardized and reproducible pipelines to preprocess, harmonize, and combine datasets.
- Develop, test, and maintain customized software tools and pipelines that support automated and interactive image analysis workflows.
- Apply deep learning (e.g., CNNs, autoencoders) and traditional machine learning methods (e.g., SVM, random forest, gradient boosting) to build predictive and classification models using imaging and other biomedical data.
- Implement image preprocessing steps including normalization, denoising, spatial registration, and segmentation of regions of interest.
Other
- Hybrid: 80% onsite and 20% remote in Texas
- works effectively in multidisciplinary scientific teams
- Collaboration with Scientific and Clinical Teams
- Documentation and Reporting
- Data Management and Infrastructure Support
