Knowledge of ligand docking and sampling techniques
The role involves joining AstraZeneca's Oncology R&D to integrate cutting-edge AI tools with physics-based simulation methods for precise drug discovery predictions
Job Summary
The role involves joining AstraZeneca's Oncology R&D to integrate cutting-edge AI tools with physics-based simulation methods for precise drug discovery predictions.
Candidates will be responsible for designing molecular dynamics simulations, creating machine-learning ready datasets, and collaborating globally with experimental colleagues.
The position offers a collaborative environment encouraging the publication of high-impact scientific work and presenting at leading conferences.
Matching Summary
The role involves joining AstraZeneca's Oncology R&D to integrate cutting-edge AI tools with physics-based simulation methods for precise drug discovery predictions.
Skills & Requirements
Must-have
PhD in Computational Chemistry or Biophysics
Experience with protein molecular simulations
Knowledge of ligand docking and sampling techniques
Proficiency in Python and RDKit programming
Understanding of ADME properties and drug likeness
Nice-to-have
Experience with large-molecule antibody simulations
Hands-on experience with coarse-grain MD approaches
Background in generative AI/ML methods for chemistry
Experience with cloud computing and GPU acceleration
Peer-reviewed publications in computational chemistry
Key Requirements
PhD required in Computational Chemistry, Biophysics, or Structural Biology
Proven experience setting up and analyzing protein simulations
Working knowledge of quantum mechanics and ML/AI methods