Overview
Applications are invited for a Research Assistant (pre‑doctoral) or Research Associate (post‑doctoral) to conduct world‑leading research on Leveraging Ontological Knowledge with Argumentative Agentic AI to Accelerate Chemical Development under the direction of Prof Alexei Lapkin.
The project is a collaboration with Prof Francesca Toni from Imperial College London, Dr Antonio Rago from King’s College London and industrial collaborators within the AIChemy Hub (https://aichemy.ac.uk/). The successful candidate will focus on extending the ontological knowledge base of chemical process development and extending the simulation environment of process models, recently developed in the group of Prof. Lapkin based in Cambridge and in Singapore (in Cambridge Centre for Advanced Research and Education in Singapore).
The successful candidate will contribute to the development of reinforcement learning agents operating with the knowledge base and the simulation environment. The developed knowledge base will provide data for advanced argumentative agentic AI based on reinforcement learning being developed at Imperial College London and King’s College. The project will develop an approach of including ontological knowledge base and expert knowledge within a multi‑agentic AI framework.
The post is based in the Department of Chemical Engineering and Biotechnology at the University of Cambridge. This is a leading department of Chemical Engineering among UK Universities. The successful applicant will join the Sustainable Reaction Engineering (SRE) research group, led by Prof Lapkin. The group is working on many aspects of transition towards sustainable chemistry, including digital transformation of R&D and manufacturing (lapkingroup.com).
The successful applicant will need to interact and collaborate with the other partners from Imperial College London, King’s College London and industry, as well as to engage with members of the AIchemy Hub, with expertise in AI and/or chemistry.
Duties and Responsibilities
The position offers an exciting opportunity for conducting internationally leading and impactful research in chemical process development supported by AI. The postholder will be responsible for researching, shaping and delivering solutions based on chemical and process ontologies, the concept of knowledge graphs, simulation of models within Python environment, working in cloud computing environment and will be expected to submit publications to top‑tier conferences and journals in Chemistry / Chemical Engineering and AI.
Qualifications
- A Master’s degree (Research Assistant) or PhD degree (Research Associate) in chemical engineering or a related area.
- Familiarity with advanced process modelling and simulation using first principles models.
- Familiarity with machine learning, coding in Python.
- Excellent communication skills and ability to work with others.
- Ability to organise your own work and set priorities to meet deadlines.
- Willingness to travel to conferences and meetings of the project and of the AIChemy Hubs.
- Strong background in chemical reaction engineering with a focus on first principles models and simulation using Python, and experience in machine learning, for example reinforcement learning, including a proven publication track‑record, in at least two of the following areas, as well as ability and willingness to become familiar with the others: process modelling and simulation, machine learning, reinforcement learning, ontologies.
Employment Details
Appointment at Research Associate level is dependent on having a PhD; those without a PhD will be appointed at Research Assistant level. Those who have submitted but not yet received their PhD will be appointed at Research Assistant level, which will be amended to Research Associate once the PhD has been awarded.
Fixed‑term: The funds for this post are available for 2 years in the first instance.
Equal Opportunity
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.
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