Postdoctoral Positions
Contract Period : 01/01/2023 – 31/12/2025 (3 years)
Job duties & Responsibilities:
The project aims to develop a new generation of RNA therapeutics. The goal of the computational part of the project is to develop methods for predicting RNA tertiary structures and optimizing RNA stability.
The recent breakthrough of AlphaFold2 in accurately predicting protein structures paves the way for applying deep learning techniques to grand challenges in biology. However, it is critical to emphasize that learning RNA tertiary structures is significantly more challenging than protein structures. Only 15,000 chains in 5,000 RNA structures are present in the PDB, 30-fold less than the 150,000 training proteins. The lack of experimental training data requires us to: (1) augment the training set with new experimental data, (2) design more theoretically expressive learning techniques than AlphaFold2, which rely on a standard supervised learning framework and a classical Transformer-based architecture.
In this role, one will be part of a multi-disciplinary team consisting of computer scientists, biologists, and bioinformaticians. Duties include primarily developing new AI methods for RNA structure predictions.
Requirements:
- PhD in Computer Science, Math, or a related discipline
- Strong programming skills (i.e. C/C++, Rust, Python)
- Knowledge of modern supervised, unsupervised and/or reinforcement learning methods
- Active knowledge of PyTorch, Tensorflow or similar deep-learning frameworks
- Excellent communication skills in written and spoken English