Markov regime-switching models for control and elimination of infectious diseases in livestock.

Are you excited by using data science in disease control? If you are, then we are looking for a skilled and motivated individual to join our team developing Monte Carlo based approaches to Bayesian inference to inform disease surveillance for control and eradication of infectious diseases in data-poor scenarios. This project is funded by BBSRC as part of an international collaboration to tackle “end-game” scenarios in eradicating infectious diseases with a focus on livestock in Kenya and Turkey.

You must have a strong interest and track record to PhD level in Bayesian inference and computationally intensive statistical methods such as Markov-chain Monte Carlo and Approximate Bayesian Computation applied to epidemiological data. You must have a high level of proficiency in a data-orientated scripting language such as R or Python, and be committed to developing high quality software implementations of your methods using modern software development techniques such as functional programming (e.g. TensorFlow, PyTorch), packaging systems (e.g. CRAN, PyPI), and Git version control.

Desirable (though not essential) skills are familiarity with epidemic modelling, Gaussian processes, and accelerated computational hardware (GPU, manycores, etc).

Type
Postdoc
Institution
Lancaster University
City
Lancaster
Country
United Kingdom
Closing date
June 30th, 2022
Posted on
June 7th, 2022 15:35
Last updated
June 7th, 2022 15:35
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