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- Tasks: Develop Bayesian machine learning models to predict drug toxicity in various organs.
- Company: Join AstraZeneca, a leading global pharmaceutical company in Cambridge.
- Benefits: Competitive salary, comprehensive training, and support from top academic advisors.
- Other info: Collaborate with experts and present findings at key conferences over a 3-year programme.
- Why this job: Make a real impact in drug discovery while pursuing your own research interests.
- Qualifications: PhD in relevant fields and strong skills in R or Python, with knowledge of Bayesian statistics.
The predicted salary is between 35000 - 45000 £ per year.
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.
Postdoc in Bayesian machine learning, AstraZeneca, Cambridge, UK employer: The International Society for Bayesian Analysis
Contact Detail:
The International Society for Bayesian Analysis Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Postdoc in Bayesian machine learning, AstraZeneca, Cambridge, UK
✨Tip Number 1
Network like a pro! Reach out to people in your field, especially those at AstraZeneca or similar companies. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a presentation that highlights your work with Bayesian machine learning. This is your chance to shine and demonstrate how you can contribute to their projects.
✨Tip Number 3
Practice makes perfect! Get ready for interviews by doing mock sessions with friends or mentors. Focus on explaining complex concepts in simple terms, as you'll need to communicate your ideas clearly.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're serious about joining the team at AstraZeneca.
We think you need these skills to ace Postdoc in Bayesian machine learning, AstraZeneca, Cambridge, UK
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to highlight your experience in Bayesian statistics and machine learning. We want to see how your skills align with the role, so don’t hold back on showcasing relevant projects or research!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about drug discovery and how your background makes you a perfect fit for our team. Let’s see your personality come through!
Showcase Your Technical Skills: Since we’re looking for expertise in R or Python, make sure to mention any specific projects where you’ve used these languages. If you’ve worked with Bayesian software like Stan or PyMC3, give us the details!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way to ensure your application gets to the right people. Plus, it shows you’re serious about joining our innovative team at AstraZeneca.
How to prepare for a job interview at The International Society for Bayesian Analysis
✨Know Your Bayesian Stuff
Make sure you brush up on your knowledge of Bayesian statistics and machine learning models. Be ready to discuss how you've applied these concepts in your previous work, especially in predicting drug toxicity or similar projects.
✨Show Off Your Coding Skills
Since the role requires excellent knowledge of R or Python, prepare to demonstrate your coding skills. You might be asked to solve a problem on the spot, so practice coding challenges related to data analysis and Bayesian methods.
✨Understand the Bigger Picture
Familiarise yourself with AstraZeneca's work in drug discovery and development. Being able to connect your research interests with their projects will show that you're genuinely interested in contributing to their goals.
✨Prepare for Collaboration Questions
As this role involves working with leading academics and presenting findings, think about your past experiences in collaborative environments. Be ready to share examples of how you've worked with others to achieve research objectives.