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Laboratory Scientist, Genomics (Fixed Term Contract)

Employer
DeepMind
Location
London (Central), London (Greater) (GB)
Salary
Competitive salary applies
Closing date
Oct 14, 2022

View more

Laboratory Scientist, Genomics (Fixed Term Contract)

DeepMind Science Lab at the Francis Crick Institute, London

 

At DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

Snapshot

DeepMind is pursuing a range of research programs in the biological sciences that strive to make ground-breaking advances on fundamental problems through the application of machine learning (ML) and artificial intelligence (AI) techniques to biology! As part of this effort, DeepMind has started a DeepMind research laboratory, located at The Francis Crick Institute. The DeepMind laboratory will provide experimental validation of molecules designed using ML.

DeepMind is looking to hire a laboratory research scientist (LRS), specialising in the broader field of genomics, on a one year contract. The primary responsibility of the role is to collaborate with computational teams to conduct biological experiments in an end-to-end manner, from initial experimental design to final data collection. The ideal candidate will also provide support across the fields of molecular biology, biochemistry and structural biology, as part of a fundamental research program at the intersection of biology and ML.

About Us

DeepMind is a subsidiary of Alphabet Inc., working on fundamental research in AI. The DeepMind Science team strives to bring AI to bear on some of today’s most foundational scientific challenges in the natural sciences, including biology. A recent example of this is the AlphaFold 2 machine learning system[1], which is able to accurately predict protein structure, given a sequence profile. Please note that DeepMind is a distinct organisation from Isomorphic Labs, an Alphabet company and commercial venture.

The role

Key responsibilities:

  • Independent design, implementation, and execution of high throughput experiments in human cells and model organisms.
  • Cell profiling through transcriptomics, proteomics, or cell biological assays.
  • Cell biological assay development.
  • Experimental iteration with machine learning experts.
  • Scientific and technical support of ongoing projects in the lab.
  • Training of junior researchers in laboratory techniques.

About you

To set you up for success as a Laboratory Research Scientist at DeepMind, we look for the following skills and experience:  

  • PhD in a relevant biological field (e.g. genetics, cell biology, molecular biology).
  • Extensive hands-on wet lab experience either in academia or biotech. In particular, practical experience with high-throughput sequencing.
  • Experience with high-throughput genome editing.
  • Experience with a range of molecular biology techniques (DNA, RNA extraction, PCR and RT-qPCR as well as cellular assays such as Flow cytometry).
  • Ability to design cell biological assays in eukaryotic cells.
  • Skilled in collaboration and teamwork.
  • Excitement about the application of ML to fundamental problems in biology.

In addition, the following would be an advantage:

  • Relevant publication record.
  • Knowledge of proteomics and phosphoproteomic workflows.
  • Experience with model organisms (e.g. Yeast, Fly).
  • Experience with biological data analysis for large datasets.
  • A passion for biology and AI!

Competitive salary applies.

 

[1] Highly accurate protein structure prediction with AlphaFold, Jumper, J. et al. Nature 596, pp. 583–589 (2021).

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