**Job Description**
This PhD position is tailored for a motivated candidate passionate about the intersection of computational science, machine learning, and drug discovery. The role focuses on pushing the boundaries of structure-based drug design using cutting-edge machine learning techniques, involving the development of state-of-the-art machine learning models for the analysis of chemical or biochemical data related to structural biology.
**Skills & Abilities**
• Hands-on experience developing state-of-the-art machine learning models, particularly deep neural networks.
• Experience with graph neural networks (highly valued).
• Background including past or present projects centered on machine learning analysis of chemical or biochemical data, especially relating to structural biology.
• Ample experience with Python programming.
• Demonstrable experience with popular machine learning frameworks such as PyTorch or scikit-learn.
• Familiarity with the PyTorch Geometric library (preferred).
• Experience with molecular dynamics simulations or molecular docking studies (preferred).
• Experience with protein structure predictions methods and co-folding methods (e.g., AlphaFold or Boltz models) (preferred).
• Strong software development skills in Python, including software package development (preferred).
**Qualifications**
Required Degree(s) in:
• Computer Science
• Computational Chemistry
• Bioinformatics
• Cheminformatics
Note: We’ve analyzed the actual job post using AI, for more details visit the original job post by clicking on “Apply Now”!
