Research Associate in Machine Learning

There is an amazing opportunity to join the Smith School of Enterprise and the Environment (SSEE).

We are seeking a Research Associate in AI/Machine Learning with expertise in large language models. Who will engage in internationally leading research in the analysis of complex, text data at scale. They will bring state of the art machine learning to the heart of geospatial data analytics with textual reports. They will also bring Bayesian and network modelling to the analysis of complex value-chains, aiding to identify investment impacts on climate and the environment.

The post holder’s work will produce open, interpretable, (asset-level) datasets and metrics of corporate impact and dependency with commensurate levels of uncertainty and environmental risk to help project partners and stakeholders align finance with sustainability, a necessary condition for tackling the environmental and social challenges facing humanity, to manage the risks and capture the opportunities associated with the transition to global environmental sustainability.

Reporting to Lead, Machine Learning, Oxford Sustainable Finance Group

To be a successful candidate will need to hold a relevant PhD/DPhil in information engineering, computer science, statistics or a commensurate discipline with specialisation in machine learning. Acquire sufficient specialist knowledge in machine learning to work within established research programmes, and sufficient specialist knowledge in machine learning to develop research questions and novel methodologies. Demonstrate practical experience with state-of-the-art natural language processing large language models alongside experience in computer programming of deep neural networks at scale and familiarity with Pytorch on a cloud computing platform (e.g., Google Cloud). A strong academic publication record concomitant with experience and familiarity with the existing literature and research in the field.

Enquiries may be directed to steven.reece@ouce.ox.ac.uk.

Applications for this vacancy should be made online and you will be required to upload a CV and supporting statement as part of your application, explaining how you meet the essential and desirable criteria for this post.

The closing date for applications is midday on Monday 27^th May 2024, and interviews will be held week commencing 3^rd June 2024.

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