Postdoctoral Fellow (deep learning in neuroimaging)
Posted 08 Feb 2021
UT Health San Antonio
San Antonio, Texas
UT Health is looking for a postdoctoral fellow who is willing to research more deep and transfer learning methods in large cohort-based studies.
Alzheimer’s disease and other dementias are heterogeneous conditions, which makes differentiating between them and their subtypes very challenging. UT's goal is to use neuroimaging data and deep learning to help uncover and detect specific pathologies and patterns emerging in early Alzheimer’s disease. In this position the challenge will be to develop new deep-learning architectures and algorithms that allow current pathology detection and prediction of possible future disease trajectories.
The work environment will be the Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC). UT builds advanced neuroimage analytical techniques to derive discovery. Data-driven approaches are of special interest in the lab, as machine learning and machine intelligence will guide the scientist towards the finding. UT's tools help delivering precise diagnostics on an individual’s level and ultimately could guide treatment progress.
The lab is part of the Biggs Institute, with the mission of establishing an interdisciplinary, integrated program to provide comprehensive clinical care and undertake innovative and important research into the prevention and treatment of Alzheimer’s Disease and other neurodegenerative conditions, including vascular contributions to dementia, Parkinson’s disease and frontotemporal dementia. It has strong institutional and community support and will benefit from existing resources within UTHSA such as the Barshop Institute for Longevity and Aging Studies, the Center for Biomedical Neuroscience, the School of Nursing, the Cancer Center, and the Research Imaging Institute along with the San Antonio campus of the UT Health Houston School of Public Health.
- Develop, test and validate novel architectures and algorithms for deep (transfer) learning with neuroimaging data.
- Apply your validated methods to large scale research and real life everyday clinical routine neuroimaging data.
- Willingness to work in teams, within NAL, BINC and Biggs and with national and international collaborators.
- Communicate research results to the larger communities through publications in international conferences and journals.
- Work with great deal of independence in achieving research goals.
- A Ph.D. degree in artificial intelligence, machine learning, computer vision, or medical image analytics with solid experience in deep learning; experience in neuroimaging and dementia research is a plus.
- Great eagerness to solve scientific problems.
- Strong programming skills, e.g. in Python, R, C++ and Java. Experience with Python deep learning toolboxes and high-performance computational facilities could be a plus.
- Excellent record of publishing in relevant high-quality journals in the above fields.
- Excellent communication abilities in English; spoken and written.