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PhD studentship in Catalysis and Machine Learning

   Department of Chemical Engineering

   Saturday, December 31, 2022  Funded PhD Project (UK Students Only)

About the Project

Introduction: Transition to net zero demands replacement of non-renewable resources with renewable alternatives. In the chemical industry, a promising solution is to substitute petroleum for annually renewable and non-edible organic matter (“biomass”). In fact, along with being renewable, biomass is highly functional and chemically diverse, giving it excellent potential as raw materials for chemicals. Unfortunately, the chemical make-up of biomass is vastly different to petroleum, and hence chemical processes that have undergone 100 years of trial-and-error improvement in the petrochemical industry are not suitable for this new type of chemistry. This means we need new processes with new properties to transition to a net zero society, and these processes need to be developed quickly.

Outline of PhD: This PhD will combine classical catalysis studies with modern machine learning methods, with the aim of developing new chemical processes to convert biomass into compounds of commercial relevance. The project integrates elements of applied catalysis, materials chemistry and reaction engineering with machine learning, optimisation, and data-driven process design. The project will entail a wide range of methods, including material preparation, catalytic testing, analytical studies, operando spectroscopy and process simulation, alongside digital informatics (machine learning, AI), and will be performed under the guidance of two investigators (Dr. Ceri Hammond and Dr. Antonio del Rio Chanona).

Candidate description: We are seeking exceptionally talented and motivated students to join our research teams. Students should hold – or soon expect to obtain – a first-class or upper second-class degree (or equivalent) in chemistry, chemical engineering or a closely related topic. This PhD project is suited for candidates interested either in applied catalysis or computational methods applied to chemical systems (e.g. optimisation and machine learning), as you will work together with other group members in a collaborative effort. Previous knowledge of catalysis, inorganic materials chemistry, and computational methods (ML, optimisation) is useful but is not a requirement.  

Application procedure: Candidates should send a CV and a personal statement directly to Ceri Hammond (). The personal statement should include the rational for applying for the position, a summary of previous research experience, and an overview of their suitability for the position.

Funding situation: Our team has one fully-funded 42 month PhD studentships available to students who classify as a “home student”, as defined by UKRI. The recruited student will commence their PhD studies in October 2022. 

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