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Job description
Job summary
An exciting new opportunity has arisen to join our Translational Modelling Hub within the MRC Centre for Global Infectious Disease Analysis at Imperial College London.
The postholder will contribute to projects targeting malaria control and eradication, focusing on applying advanced machine learning methods to malaria planning and policy-making. This task involves merging malaria transmission modeling with machine learning model emulation techniques, while maintaining close collaboration with our research team and external stakeholders to guarantee the models are effectively used in real-world settings.
The role includes collaborating with a diverse team comprising research fellows, associates, and software engineers, and engaging with external collaborators such as national malaria control programs and global health partners. The postholder will be responsible for maintaining regular interactions with these stakeholders, ensuring the alignment of the project’s objectives with stakeholder needs.
Duties and responsibilities
- Develop and apply advanced machine learning model emulation methods to integrate with malaria transmission models, enhancing strategic decision-making in malaria control and eradication.
- Collaborate with the malaria modelling group and external stakeholders, focusing on the practical application of models for policy planning and response.
- Contribute to software development and maintenance, manage project timelines and deliverables, and participate in the creation of supporting documentation and presentations.
Essential requirements
- Hold an MSc or PhD in Machine Learning, Computational Science, Statistics or related quantitative discipline or relevant industry experience.
- Experience in R or Python programming languages for designing, implementing, training, and testing machine learning models.
- Experience in packaging and documenting software for others to use.
- Up-to-date knowledge of machine learning techniques and their applied use in data analysis and prediction.
Further information
Fixed Term Contract for 1 year in the first instance.
For more detailed information, you can refer directly to the job description document. Should you require any further details on the role please contact: Dr Peter Winskill – [email protected] .
Hybrid working may be considered for this role. Staff working in roles that are suitable for hybrid working will normally be expected to work 60% of their time onsite. The opportunity for hybrid working will be discussed at interview.
More information is available on the following web page: Work Location Categories (from 30 September 2023) tendersglobal.net Administration and support services tendersglobal.net Imperial College London
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