WHAT WE ARE LOOKING FOR

• A PhD (or equivalent) in an area pertinent to the subject area, e.g., Computer Science, Applied Mathematics, Engineering Physics, or Chemical Engineering. Alternatively, equivalent research or industrial experience.*
• You must have experience in algorithms development
• You must have proven strong publication track-record in machine learning, computational optimisation, process systems engineering, or a closely-related discipline
• You will be expected to contribute towards developing and maintaining open source software related to this project (https://github.com/cog-imperial)
• Ability to conduct a detailed review of recent literature

Please see job description for a full list of requirements.

• Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant, salary range £43,863 - £47,223 per annum.

AVAILABLE DOCUMENTS

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Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.
We reserve the right to close the advert prior to the closing date stated should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.
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ABOUT THE ROLE

We are looking for a motivated Research Associate in Bayesian Optimisation & Experimental Design to work with Professor Ruth Misener and Dr Calvin Tsay in the Department of Computing.
The Department of Computing at Imperial College London is a leading department of Computer Science among UK Universities. The department has achieved top results in each of the research assessment exercises undertaken by the Higher Education Funding Council for England. There are over fifty academic staff members actively involved in research, creating a lively and stimulating atmosphere. The department is located in central London, next to Hyde Park and the museums of South Kensington.

WHAT YOU WOULD BE DOING

You will conduct research at the intersection of computational optimisation, machine learning, and experimental design of new chemistry. You will develop Bayesian optimisation and other active learning approaches for experimental design with a focus on real-world systems. While most of the work will be computational or mathematical, you will also have the opportunity to visit BASF in Ludwigshafen, Germany for three months to try out the new algorithms in the lab.