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Postdoc opportunity at AstraZeneca in Cambridge, England, in Bayesian Machine Learning using Stan!

Here it is:

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. . . .

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.

One Comment

  1. Anoneuoid says:

    known causal relationships and known toxicity mechanisms

    I’ve seen that major problem with training ML models on bio data is that the “ground truth” is often unreliable. That’s in the case of simpler problems like segmenting/classifying images, I have wonder about what will happen when you feed in higher level stuff like “toxicity mechanism” which is going to be based on a series of NHST results.

    I think there is going to be a very strong temptation for people with jobs like this post-doc to just take the literature as fact here. After all, collecting and interpreting that data is not supposed to be their job. This could lead to big problems down the road though.

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