Two-year Postdoctoral Fellowship in Machine Learning, Information Security, and Trustworthy AI
Privacy-Preserving, Robust, and Explainable Federated Learning Framework for Healthcare System
Total Salary for Two Years: $90,000
Open date: 08/16/2022 – Closing date: 12/31/2022
Project Description and Responsibility
The project focuses on designing a trustworthy federated learning (FL) framework for the healthcare system with theoretical guarantees for its privacy, robustness, and agent-level data valuation and explainability, aiming to make the healthcare systems more efficient and trustworthy.
The successful candidate will develop novel approaches for federated learning in medical systems. The candidate will be embedded in a highly international research group offering wide expertise in machine learning on medical dataset. Also, we provide the candidate with direct access to computing facilities optimized for machine/deep learning.
We seek an outstanding candidate with a solid theoretical foundation in machine/deep learning, information theory, and information security while being motivated to apply the theory to practical datasets and problems. The candidate should have strong written and oral communication (in English) and already have documented experience in machine/deep learning. Expertise in computer security is an advantage but not a requirement. The candidates will also contribute to interdisciplinary projects within VISHC with multidisciplinary team members. Candidates with a Ph.D. in Computer Science, Electrical Engineering, or a related discipline are preferred.
How to apply
Applicants should submit a CV, a research summary, and the names of three recommenders who can write letters on their behalf. This information should be sent to VISHC (firstname.lastname@example.org) and Prof. Kok-Seng Wong (email@example.com). Review of applications will begin immediately and continue until the position is filled.