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Research Associate in Probabilistic Machine Learning for Modelling Biological Systems - Manchester, United Kingdom - The University of Manchester
Description
Applicants are invited for the post of Research Associate in probabilistic machine learning for modelling biological systems.
The post-holder will join a team of computational, mathematical and experimental biologists funded by a Wellcome Trust Discovery Award "Defining the spatiotemporal gene expression dynamics controlling embryonic patterning". They will be responsible for developing and applying probabilistic machine learning methods for modelling high-resolution spatio-temporal data (e.g. live cell imaging and spatial transcriptomics) collected during embryonic development. We are applying a range of methods, including but not limited to Gaussian process modelling, hidden Markov models and deep learning approaches. One promising approach that we would like to explore is the development of latent force models that can capture the spatio-temporal developmental dynamics, integrating gene regulatory network modelling with Gaussian process inference. We are generally interested in approaches that combine data-driven modelling (machine learning) with mechanistic modelling (systems biology). Models developed will be used to gain mechanistic insights and design/interpret perturbation experiments that will then feed back to further improve the model.
The successful applicant will have a PhD (or equivalent) with a significant computational and/or statistical element and will have experience of probabilistic modelling and/or machine learning. A strong interest in biology is essential. The candidate should also have a good scientific publication record given career stage and be self-motivated, hard-working and able to work in a team.
What can you expect in return
The University will actively foster a culture of inclusion and diversity and will seek to achieve true equality of opportunity for all members of its community.
What you will get in return:
As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
Our University is positive about flexible working you can find out more here
Hybrid working arrangements may be considered.
Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.
Any recruitment enquiries from recruitment agencies should be directed to
Any CV's submitted by a recruitment agency will be considered a gift.
Enquiries about the vacancy, shortlisting and interviews:
Name: Professor Magnus Rattray
Email:
General enquiries:
Email:
Technical support:
This vacancy will close for applications at midnight on the closing date.
Please see the link below for the Further Particulars document which contains the person specification criteria.
