Research Associate

Key Skills

Climate Risk modeling, Machine learning techniques, Natural Language Processing (NLP), Python Programming, SQL

Job Description

Department of Biology, 11a Mansfield Road, Oxford, OX1 3SZ and Department of Statistics, 24-29 St Giles', Oxford, OX1 3LB

We are seeking to appoint a Research Associate in Machine Learning with a specialism in natural language understanding or information retrieval. The Research Associate will engage in internationally leading research in the analysis of heterogeneous text-based data at scale; he/she will bring state of the art machine learning to the heart of nature recovery, specifically to track the rapidly evolving field via published scientific articles or web-based text reports. The Researcher will achieve this by advancing state-of-the art deep learning techniques for text analysis and summarisation.

The researcher will work in a team of machine learning experts within the Leverhulme Centre for Nature Recovery. The Leverhulme Centre for Nature Recovery (LCNR) is being 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, as a Research Associate in Machine Learning for Nature Recovery working closely with the Nature-based Solutions Initiative (Department of Biology), you will be collaborating 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). Your work will produce state-of-the-art methodologies and algorithms that identify effective ways of working with natural ecosystems within the published literature, track sentiment towards restoration initiatives and filter key scientific reports. Outputs will form the basis of guidance and tools for decision-makers and land managers. Currently, it is hard for decision makers to access the best evidence, partly because that evidence is scattered among 1000s of journals and across several disciplines. Manual systematic reviews are extremely time-consuming and, as a result, poor decisions are being made that affect our futures. Deployment of ML approaches to speed up this process is urgently needed.

You will prepare and publish in high quality academic publications and regularly write and publish articles in peer-reviewed journals and conferences. You will participate actively in research within the LCNR and the Nature-based Solutions Initiative, developing collaborations with others. You will contribute to teaching, including undergraduate and MSc/MPhil courses within the Department of Statistics.

The successful candidate must hold, or be close to completion of, a relevant PhD/DPhil with, ideally, post-qualification research experience in machine learning or statistics with a specialism in natural language understanding or information retrieval. You must have a strong academic publication record concomitant with your experience, and familiarity with the existing literature and research in natural language understanding machine learning. You will have sufficient specialist knowledge to develop novel research questions and methodologies.

The University of Oxford is committed to equality and valuing diversity. All applicants will be judged on merit, according to the selection criteria.

This post is full time and available immediately.

The closing date for applications is 12.00 noon on 5th January 2023, interviews are likely to be scheduled for January.

• Contact Person : HR
• Contact Phone : 
• Closing Date & Time : 05-Jan-2023 12:00
• Pay Scale : STANDARD GRADE 7
• Contact Email : HR@Biology.ox.ac.uk
• Salary (£) : Grade 7: £34,308 - £42,155 per annum