Postdoctoral Research Associate in Computational Protein Engineering

Grade UE07: £40,497 - £48,149 per annum

College of Science and Engineering/School of Biological Sciences/Institute of Quantitative Biology, Biochemistry and Biotechnology

Full-time: 35 hours per week

Fixed-term: 12 months 

The Opportunity: 

We are looking for exceptionally talented and motivated computational scientist to take up a fully funded position as Postdoctoral Research Associate (PDRA) in computational protein engineering with Prof Giovanni Stracquadanio and in collaboration with OccamBio Ltd. 

We are an interdisciplinary group based at the School of Biological Sciences interested in addressing challenges in healthcare and biotechnology (www.stracquadaniolab.org). Our goal is to reverse engineer biological systems in physiological and pathological state, to learn the rules for rapidly and efficiently engineering new therapies. To do that, we use the most disruptive technologies of the last 20-years: machine learning and synthetic biology. 

Our team is highly interdisciplinary, including mathematicians, biologists, and biochemists, enjoys a fast-paced collaborative working environment and aims at continuously improving our strong publications and funding track record.

We routinely engage with industrial stakeholders, and we have ongoing collaborations with Fujifilm Diosynth, Opentrons, Lonza and Neochromsome. 

In collaboration with OccamBio Ltd, we aim at designing deep learning models to engineer thermostable growth factor proteins to make cultivated meat sustainable; to do that, we will use public available data and state-of-the-art protein language models. Our group is strongly committed to open and reproducible science; thus, we strive to deliver our models as user-friendly software packages to maximise their adoption by the scientific and industrial community. 

Your skills and attributes for success: 

• Strong track record in deep learning applied to protein engineering, preferably language models.
• Strong track record in biological data analysis.
• Excellent problem-solving skills.
• Strong planning and management skills.
• Excellent written and verbal communication skills.

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