|Job Type:||Full Time|
The Station B team at Microsoft Research Cambridge (UK) aims to transform the development and production of life-saving therapies, including cancer immunotherapy, via world-class research in synthetic and computational biology. The team has an exciting opportunity for a senior scientist to develop methods for programming biology, in partnership with domain experts from leading companies such as Oxford Biomedica and Novartis. The candidate will be responsible for developing and driving an exciting research agenda, working in a multi-disciplinary environment that also includes colleagues with expertise in machine learning, systems and biology.
The ideal candidate will have strong intellectual curiosity and passion to solve real-world problems in health and life sciences. The focus of the position is to work with a team of researchers and engineers to develop new methods to facilitate computational modelling of cell behaviour, including cell growth, metabolism, intracellular protein interactions and the behaviour of synthetic gene circuits. This will involve developing automated model approximation methods that can be applied to large scale proteomics and transcriptomics datasets, and integrating mechanistic models with machine learning techniques. The responsibilities include:
- Biological and computational modelling, developing methods for modelling of cell behaviour.
- Engaging with domain experts and scientists to shape the research for maximum impact and relevance.
- Publishing work in leading scientific journals and conferences.
- Supervising PhD students and interns.
- PhD or equivalent in a relevant field with a focus on quantitative modelling of biological systems.
- Experience with dynamical modelling approaches including ordinary differential equations and related numerical methods. Demonstrated ability to develop predictive models based on experimental data.
- Experience in dynamical systems theory and model reduction techniques in the context of cell biology.
- Experience with machine learning models applied to biological datasets.
- Ability and desire to work in cross-disciplinary teams.
- Good collaboration skills.
- Track record of executing on an innovative research agenda, including publications in leading scientific journals.
Desirable additional skills:
- Experience working with stochastic models of biological systems, including stochastic differential equations.
- Experience with synthetic biology workflows.
- Experience in processing and interpreting biological data, and in designing experiments to test model predictions.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.
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