Associate Scientist, Postdoctoral Fellow, AI/ML and Agentic AI for Drug Discovery

What you'll do

• Design, execute, and interpret research to benchmark standard AI/ML methods across drug discovery datasets.
• Develop and drive an independent research project to design and evaluate AI agent frameworks for drug discovery.
• Refine agentic AI workflows to improve transparency, robustness, and decision-making.
• Analyze complex drug discovery datasets using statistical and machine learning tools.
• Apply modern AI/ML methods to automate and accelerate predictive modeling workflows.
• Present research findings at internal scientific forums, external conferences, and in peer-reviewed journals.

What we're looking for

• Must currently hold a PhD or receive a PhD no later than spring 2027.
• PhD must be in Machine Learning, Computer Science, Computational Chemistry, Cheminformatics, Chemical or Biomedical Engineering, Applied Mathematics, or a related discipline.
• Demonstrated expertise in machine learning, cheminformatics, or computational chemistry.
• Strong record of scientific achievement evidenced by peer-reviewed publications and presentations.
• Hands-on experience building, training, and rigorously evaluating machine learning models in Python.
• Experience designing, conducting, and interpreting computational experiments, including model benchmarking.
• Strong quantitative and analytical skills, including statistical analysis and data interpretation.
• Experience with large language models and agentic AI (preferred).