Computational Chemistry/Machine Learning/AI Scientist

Reference: SLSC0698

Grade 7 -8 (£32,817 – £49,533)

Closing date: 29 February 2020

Artificial Intelligence (AI) and Machine/Deep Learning (ML,DL) are revolutionising the drug discovery process and the way compounds are optimised to achieve the desired biological activity and pharmacological profile. The DDU Computational Chemistry Team is looking for a highly motivated Scientist with Machine/Deep Learning experience.
The successful candidate will join a team focused on the development of a platform integrating ML/DL/AI and physics based computational chemistry methods to drive and accelerate compounds biological activity and pharmacological profile optimisation.

The post is available immediately for two years. Starting salary from £32,817 to £49,553 depending on experience. A relocation allowance is available.

Necessary skills and experience

  • Highly motivated, self-directed individuals professionally qualified with a PhD in computational chemistry, bio/cheminformatics, or a related topic or a BSc and significant experience in working in the field.
  • Experience in the application of AI/ML/DL methods to drug discovery
  • Advance programming in Python
  • Strong scientific background with an understanding of the Drug Discovery process
  • Ability to work independently, and in close collaboration with discovery scientists as part of a team
  • Good time management, organizational and communication skills.
  • Strong interpersonal, communication and presentation skills


For Grade 8

  • The candidate will have had experience running projects and line management experience


Desirable Experience

  • Experience in Chemoinformatics, Chemical Space enumeration and expansion
  • Experience in developing processes and tools into a flexible and user-friendly platform – web based tools development front/back-end
  •  Expertise in Multiparameter optimisation
  •  Exposure to drug discovery projects
  •  Experience in hardware setup, networking support, software deployment and configuration
  • Working knowledge of statistical modelling

For further information about this position please contact Professor Ian Gilbert at

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