Graduate Research Assistant Position - Federated Learning
The Center for Secure Artificial intelligence For hEalthcare (SAFE) is seeking a highly motivated and talented GRA with a strong focus on federated learning and its applications to genomics and electronic health records. The successful candidate will work on cutting-edge research projects, developing and implementing federated learning models using Python and PyTorch while incorporating differential privacy and homomorphic encryption techniques.
- Develop and implementfederated learning models using Python and PyTorch for genomics and electronic health record applications.
- Collaborate with teammembers to define, design,and develop novel research projects.
- Work with various datasources and APIs related to genomics and electronic health records.
- Learn and apply differential privacy and homomorphic encryption techniques to ensure data security and privacy.
- Implement automated testing and provide feedback on model performance and accuracy.
- Constantly explore, evaluate,and implement new technologies and methodologies to maximize research efficiency.
- Proficient in Python programming and experience with PyTorch or similar deeplearning frameworks.
- Ability to work in a fast-paced research environment and adapt to significant changes in work tasks.
- Willingness to learn and work with topics in differential privacy and homomorphic encryption.
- Strong problem-solving skills and ability to learn quickly and independently.
- Experience with genomics and electronic health recorddata analysis.
- Familiarity with data privacyand security concepts, such asdifferential privacy and homomorphic encryption.
- Experience with version control systems like Git.
- Prior experience with federated learning and its applications to healthcare.
Interested Applicants Submit:
Email both a summary justifying why you are qualified for the position, A current CV or Resume to: Xiaoqian.Jiang@uth.tmc.edu