Research Fellow in Artificial Intelligence
We are seeking a highly motivated research fellow with a strong mathematical background and with experience in deep learning, to conduct research on machine learning in the presence of noise of arbitrary type. The successful candidate will work closely with other team members to develop a comprehensive learning theory for noisy datasets.
The goal of this project is to develop a unified mathematical framework for dealing with arbitrary noise in a distributed network of autonomous AI systems. We shall consider arbitrary noise not just in datasets, but also in the model parameters of AI models. The scope of our project is inspired by fundamental technical challenges that must be resolved before personalized AI systems are able to interact and learn from one another (e.g. exchange AI model parameters), without any unintended failures due to noise. Specifically, we shall build a comprehensive theory for the trustworthiness of data and AI models, develop general methods for noise detection tiered by user-selected thresholds, and design robust systems for noisy distributed learning on noisy data that are computationally efficient. We shall deal with all types of noise, whether known or unknown, via a “universal” approach.
Duration of position: 1 year.
- Required: PhD degree in computer science, mathematics, or data science intensive field.
- The candidate must have a strong mathematical background and must have experience in deep learning.
- An ideal candidate would have strong programming experience in Python.
- Prior experience working with noisy datasets is a big bonus, but is not required.
Interested candidates should submit a current CV and copies of academic transcripts (from both undergraduate and post-graduate studies), directly to Ernest Chong (firstname.lastname@example.org) with the email subject header “AI Research Fellow position for project on noisy data”. We regret to inform that only shortlisted candidates will be notified.
Applications close: 30 Nov 2021 Singapore Standard Time
- Offered SalaryNot Specified
- Career LevelNot Specified
- ExperienceNot Specified
- QualificationDoctorate Degree (Ph.D.)