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The University of Texas Health Science Center at San Antonio (UT Health San Antonio)
San Antonio, Texas, United States
24 days ago

Description

The Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) are recruiting a postdoctoral fellow in deep learning and neuroimaging

University of Texas Health Science Center San Antonio (DBA UT Health San Antonio)

Are you excited about deep learning and transfer learning? Do you want to apply and develop deep learning methods to high-dimensional neuroimaging data and make new discoveries that could advance our understanding of Alzheimer’s disease? We are looking for a postdoctoral fellow who is willing to research deeper 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. Our 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, your challenge will be to develop new deep learning architectures and algorithms that allow current pathology detection and prediction of possible future disease trajectories.

Your work environment will be the Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC). We build advanced neuroimage analytical techniques to derive discovery. Data-driven approaches are of special interest in our lab, as machine learning and machine intelligence will guide the scientist towards the finding. On a broader goal, our tools help deliver precise diagnostics on an individual’s level and ultimately could guide treatment progress.

We are part of the Biggs Institute (https://biggsinstitute.org), which is being established as a flagship, free-standing institute within the University of Texas Health Science Center San Antonio (UT Health SA), 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 UT Health SA 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.

Responsibilities
• 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 your research results to the larger communities through publications in international conferences and journals
• Work with a great deal of independence in achieving research goals



Requirements

• 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. 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.

Job Information

  • Job ID: 59041323
  • Location:
    San Antonio, Texas, United States
  • Position Title: Postdoctoral Fellow in Deep Learning and Neuroimaging
  • Company Name: The University of Texas Health Science Center at San Antonio (UT Health San Antonio)
  • Job Function: Research
  • Job Type: Full-Time
  • Min Education: Ph.D.

Please refer to the company's website or job descriptions to learn more about them.

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