Senior Research Associate

The Department of Statistics and Department of Biology are recruiting for a Senior Research Associate in Machine Learning on a fixed-term basis for 36 months.

We are seeking to appoint a Senior Research Associate with a keen interest in large language models, natural language processing, information retrieval, and their use in nature recovery and addressing the impacts of climate change. The post holder will engage in internationally leading research on using large language models (LLMs) for the analysis of heterogeneous scientific text data (e.g. published scientific articles and web-based text reports), to enable researchers to track and understand the rapidly evolving field of nature recovery. The post holder will achieve this by advancing state-of-the art LLM capabilities (e.g. factuality, helpfulness, continual learning), and by taking a collaborative and full stack approach to machine learning (data collection and processing; unsupervised, supervised and RLHF finetuning; verification; deployment; and downstream analysis).

This post will work in a team of machine learning experts within the Leverhulme Centre for Nature Recovery (LCNR), which is a virtual cross-disciplinary centre established to address the challenges of deploying nature-based solutions and delivering effective nature recovery at scale in a way that addresses climate change, supports biodiversity and enhances human wellbeing. In particular, they will be working closely with the Nature-based Solutions Initiative (NbSI) at the Department of Biology and the Oxford Computational Statistics and Machine Learning (OxCSML) group at the Department of Statistics.

At the NbSI the Senior Research Associate will collaborate with a team of multidisciplinary researchers to mine the evidence base for the effectiveness of nature-based solutions to climate change mitigation and adaptation (see www.naturebasedsolutionsevidence.info*).* Their work will produce state-of-the-art machine learning methodologies and algorithms that identify effective ways of working with natural ecosystems within the published literature, track sentiment towards restoration initiatives and summarise key scientific reports. Outputs will form the basis of guidance and tools for decision-makers and land managers.

At OxCSML the Senior Research Associate will be part of a vibrant research community developing fundamental tools and state-of-the-art techniques for machine learning. They will conduct world leading research on LLMs, publish in high quality machine learning conferences and journals, and mentor MSc and DPhil students. This role will also contribute to teaching on undergraduate/masters courses within the department.

We proudly hold a Race Equality Charter Bronze Award and a departmental Athena SWAN Silver Award, which guide our progress towards advancing racial and gender equality. As part of our commitment to openness, inclusivity and transparency, we would particularly welcome applications from women and black and minority ethnic candidates, who are currently under-represented in positions of this type at Oxford.

If you would like to discuss this post and find out more about joining the academic community in Oxford, please contact Professor Yee Whye ([email protected]) or Professor Nathalie Sneddon ([email protected]). All enquiries will be treated in strict confidence and will not form part of the selection decision.

Applicants will be selected for interview purely based on their ability to satisfy the selection criteria as outlined in full in the job description. You will be required to upload a statement setting out how you meet the selection criteria, a curriculum vitae including full list of publications, and the contact details of two referees as part of your online application. (NOTE: Applicants are responsible for contacting their referees and making sure that their letters are sent to [email protected] directly by the closing date, quoting vacancy reference 170055).

Only applications received before 12.00 noon UK time on 12 January 2024 can be considered.

Interviews are anticipated to be held on 26 January 2024.