Kai Fong Ernest Chong

**Project: "Noisy distributed learning on noisy data: A unified mathematical framework for dealing with arbitrary noise"**
**(One post-doctoral position available!)** Click here for more details.

Duration of position: 2 years.

- 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.

**Project: "The Architecture of Deep Learning: An algebraic framework for automated generation of optimal neural network architectures"**
**(Two post-doctoral positions available!)** Click here for more details.

Duration of position: 2 years.

- Required: PhD degree in mathematics, with experience in commutative algebra, computational algebra, homological algebra, or a related algebraic area.
- The candidate is expected to have at least some programming experience, and must be open to learning new things (e.g. in computer science).
- An ideal candidate is excited by the possibility of applying algebraic methods (e.g. in computational commutative algebra or homological algebra) to problems in Artificial Intelligence, specifically in machine learning and machine reasoning.
- Candidates with strong algebraic backgrounds are prioritized.

**Project: "Rigidity theory and the combinatorics of simplicial manifolds"**
**(One post-doctoral position available!)** Click here for more details.

Duration of position: 3 years.

- Required: PhD degree in mathematics.
- The candidate should ideally have experience/interests in at least one of the following specific research areas:
- Combinatorial and topological aspects of simplicial complexes
- Combinatorial aspects of polytopes and polyhedral complexes
*f*-vector theory (inc. Stanley-Reisner rings of various classes of simplicial complexes)- Rigidity theory
- Commutative algebra
- Tropical geometry, intersection theory

**SUTD undergraduate students:**If you are interested in research in computational aspects of deep learning (e.g. making deep learning models train more efficiently), please contact Ernest Chong ( ) directly. You would have to commit at least 6 months.**Visiting students:**If you are interested in research in computational aspects of deep learning (e.g. making deep learning models train more efficiently), please contact Ernest Chong ( ) directly with your current CV and a latest copy of your undergraduate academic transcript. You would have to commit at least 3 months. Funding is possible. We regret to inform that only shortlisted candidates will be notified.