Senior Computational Scientist

Title: Computational Structural Biologist

Location: Cambridge

Salary: Competitive

Introduction to the role:

The Computational Structural Biology (CSB) team is growing our broad-ranging expertise encompasses structural bioinformatics, protein structure prediction, molecular dynamics simulations, free-energy calculations, protein docking, and in silico prediction of physico-chemical properties of biomolecules using advanced computational methods. The team sits as part of the wider Protein Sciences dept. which poses leading capabilities in Protein Structure Prediction, Crystallography, Cryo-Electron Microscopy, Protein Expression and Biophysics.

This role will see you collaborating with structural biologists, protein scientists, biophysicists, chemists, biologists and computational scientists across all of AZs therapy areas. You will work proactively across multiple projects within an interdisciplinary, collaborative, dynamic environment to help develop high-quality lead proteins, ultimately leading to new clinical candidate drugs.

This is an outstanding opportunity to develop and work with ground-breaking, technology and apply it to bio-therapeutic discovery and development projects in a dynamic and interdisciplinary environment.

Accountabilities:

• Predict, analyse and interpret protein structure using computational modelling tools like AlphaFold, Boltz, EvoBind, molecular dynamics simulations and ML/AI.

• Apply and develop Free-energy calculations for molecular interactions.

• Apply molecular dynamics simulations methods to understand protein dynamics and motions.

• Generate scripts and workflows to enable automated analyses.

• Apply and develop deep learning and molecular dynamics-based approaches to biologics drug discovery.

• Be part of and contribute to multidisciplinary drug discovery project teams.

Essential criteria:

• PhD in Computer Sciences applied to Biochemical systems.

• Strong expertise in free energy calculations applied to Biochemistry or protein design using AI.

• Expertise preparing and running complex molecular dynamics simulations of biochemical systems.

• Strong experience in programming with Python for molecular structures.

• Good track record of publications, strong research skills, and a history of innovation and achievements.

• Strong experience using molecular modeling packages like Biovia, MOE, Schrodinger, etc.

• Outstanding teamwork and excellent communication skills.

Desirable criteria:

• Advanced methods for free-energy calculations will be an advantage.

• Experience applying methods for protein design with ML/AI will be an advantage.

• Relevant areas of experience might include development and implementation of new deep learning methodologies and generative models.

• Experience in molecular dynamics simulations or protein structure prediction with fully open source programs is highly desired (e.g. OpenFold, ProteniX, Boltz-1, EvoBind, Rosetta, etc).

• Relevant post-doctoral experience will be advantageous, as would be experience of working in a drug discovery environment.

• Experience using high performance computing clusters and cloud computing (e.g. AWS, Azure, etc).

• Experience using multiple molecular dynamics simulation packages (Charmm. Gromacs. Etc).

What is next?

Are you ready to make a difference? Apply today and join us in our mission to improve global public health!

We welcome your applications not later than 28th February 2025.

Where can I find out more?

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AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.