Industrial Phd Student In Machine Learning And Drug Design In Molecular Ai

AstraZeneca

Gothenburg, Sweden
Machine learning for drug design
Binding affinity prediction models
Physics-informed machine learning
The overarching objective is to build physics-informed machine learning models for binding affinity prediction, enabling generative models to design chemically plausible compounds and prioritize candidates from hit discovery through to lead optimization

Job Summary

  • The overarching objective is to build physics-informed machine learning models for binding affinity prediction, enabling generative models to design chemically plausible compounds and prioritize candidates from hit discovery through to lead optimization.
  • The research time of the PhD student will be split between Lund University and AstraZeneca, Gothenburg.
  • This collaborative environment fosters knowledge sharing and provides valuable exposure to the industrial drug discovery process.

Matching Summary

The overarching objective is to build physics-informed machine learning models for binding affinity prediction, enabling generative models to design chemically plausible compounds and prioritize candidates from hit discovery through to lead optimization.

Skills & Requirements

Must-have

  • Machine learning for drug design
  • Binding affinity prediction models
  • Physics-informed machine learning
  • Computational chemistry methods
  • Python and deep learning

Nice-to-have

  • Collaborative mindset
  • Entrepreneurial skills
  • Workflow automation experience
  • Scientific research collaboration

Key Requirements

  • Master of Science degree
  • Excellent English communication skills
  • Willingness to engage with cutting-edge methods

Work Rights

Not specified

Tailored Resume

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