Postdoc in Bayesian machine learning, AstraZeneca, Cambridge, UK

The International Society for Bayesian Analysis; Cambridge; On-site; GBP 35,000 - 45,000; Full time

Job summary

A prestigious opportunity for a Postdoc in Bayesian machine learning at a leading pharmaceutical company in Cambridge, UK. You will develop innovative Bayesian models for predicting drug toxicity while participating in a comprehensive training program and collaborating with leading researchers. Ideal candidates will have a PhD in a relevant field and strong programming skills in R or Python.

Qualifications

• PhD in a relevant field.
• Excellent knowledge of R or Python, ideally both.
• Familiarity with Bayesian statistics and software like Stan and PyMC3.

Responsibilities

• Develop Bayesian machine learning models for predicting drug toxicity.
• Incorporate scientific knowledge into predictions.
• Present findings at conferences and in publications.

Skills

R

Python

Bayesian statistics

Modern Bayesian software

Knowledge in life sciences

Education

PhD in Statistics, Computer Science, Data Science, or similar

Job description

Postdoc in Bayesian machine learning, AstraZeneca, Cambridge, UK

Mar 29, 2018

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:
https://job-search.astrazeneca.com/job/cambridge/post-doc-fellow-bayesian-machine-learning/7684/7417160