PREDICTING DRUG TOXICITY WITH BAYESIAN MACHINE LEARNING MODELS
We’re currently looking for talented scientists to join our innovative academic-style Postdoc. From our centre in Cambridge, UK you’ll be in a global pharmaceutical environment, contributing to live projects right from the start. You’ll take part in a comprehensive training programme, including a focus on drug discovery and development, given access to our existing Postdoctoral research, and encouraged to pursue your own independent research. It’s a newly expanding programme spanning a range of therapeutic areas across a wide range of disciplines. What’s more, you’ll have the support of a leading academic advisor, who’ll provide you with the guidance and knowledge you need to develop your career.
You will be part of the Quantitative Biology group and develop comprehensive Bayesian machine learning models for predicting drug toxicity in liver, heart, and other organs. This includes predicting the mechanism as well as the probability of toxicity by incorporating scientific knowledge into the prediction problem, such as known causal relationships and known toxicity mechanisms. Bayesian models will be used to account for uncertainty in the inputs and propagate this uncertainty into the predictions. In addition, you will promote the use of Bayesian methods across safety pharmacology and biology more generally. You are also expected to present your findings at key conferences and in leading publications
This project is in collaboration with Prof. Andrew Gelman at Columbia University, and Dr Stanley Lazic at AstraZeneca.
Education and Experience Required:
– PhD in Statistics, Computer Science, Data Science, or similar
– Excellent knowledge of either R or Python (ideally both)
– Knowledge of Bayesian statistics
– Knowledge of modern Bayesian software such as Stan and PyMC3
– Knowledge of (or an interest in) life sciences
This is a 3 year programme. 2 years will be a Fixed Term Contract, with a 1 year extension which will be merit based. The role will be based at Cambridge, UK with a competitive salary on offer.
To apply for this position, please follow the link below:
Advert closing date – 13th May 2018