Fellowship in Machine Learning Drug Research

Position: Fellowship in Machine Learning for Drug Research
Become a Target 2035 Fellow and explore groundbreaking research in machine learning for drug discovery. Located in Toronto, this role promises a collaborative research environment with ongoing learning opportunities.

As a Postdoctoral Researcher, you will tackle complex challenges in drug discovery, focusing on systematizing data use for ML applications. Your position will involve close collaboration with labs from both the University of Toronto and Columbia University, emphasizing high-throughput screening and data-driven innovations that inform future experiments and discoveries.

Key Responsibilities:

• Develop pipelines for proteome binding site detection

• Fine-tune models on DEL and ASMS datasets

• Establish metrics for predicting binding site accuracy

• Compile a dynamic target screening list

• Facilitate collaboration between computational and experimental teams

Requirements:

• PhD required in Computational Chemistry or a related discipline

• Background in multi-fidelity optimization

• Knowledge of molecular simulations

• Proficiency with large-scale machine learning

• Experience in dealing with experimental data variability

Engage in high-impact research that sets a new standard in the drug discovery landscape with Target 2035.
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