Research Assistant​/Associate in Applied ML Researcher-Agricultural Systems

Are you interested in applying machine learning and computational methods to address real-world challenges in sustainable agriculture? We are seeking a highly motivated researcher to join a weed research programme at Newcastle University, delivering specialist expertise in machine learning, data analysis, and modelling. This role will focus on the development, implementation, and validation of advanced ML approaches for weed detection, discrimination, and management using complex biological and spectral imaging datasets.

The postholder will contribute exclusively to research, supporting large‑scale data analysis, interpretation, and modelling, as well as contributing to publications and grant deliverables in collaboration with plant scientists. You should enjoy problem solving, working with complex datasets, and translating analytical results into biological and agricultural insights. Experience in machine learning, computational analysis, and handling high‑dimensional data is essential with familiarity with agricultural or environmental applications.

You would join researchers within the Agriculture and Animal Science Group, which sits within the School of Natural & Environmental Sciences, working in the group led by Dr Ankush Prashar. The project offers scope for further development, while also providing opportunities to enhance your career through University training and development programmes. As part of our commitment to career development for research colleagues, the University has developed 3 levels of research role profiles.

These profiles set out firstly the generic competences and responsibilities expected of role holders at each level and secondly the general qualifications and experiences needed for entry at a particular level.

• Carry out research developing, implementing, and validating machine learning and computational methods for weed detection, discrimination, and management using spectral and imaging data.
• Apply principles of data science, machine learning, and statistical analysis to solve complex biological and agricultural research problems.
• Design and optimise scalable data pipelines for processing high‑dimensional spectral and imaging datasets, including feature extraction, model training, validation, and performance evaluation.
• Ensure intellectual rigour and adherence to robust ethical and data governance standards to maintain the integrity of research.
• Strong knowledge of machine learning, statistical modelling, and data science methods applied to biological or environmental datasets.
• Proven ability to analyse high‑dimensional spectral and/or imaging data for classification, detection, and discrimination tasks.
• Experience in developing, implementing, and validating computational models and algorithms using appropriate programming languages and frameworks.
• Ability to design robust analytical workflows, including data preprocessing, feature extraction, model training, and performance evaluation.
• Strong skills in critical data analysis, interpretation of results, and construction of conceptual or computational models to explain biological patterns.
• A proven track record of publishing high‑quality research outputs in relevant peer‑reviewed journals or conferences.
• Ability to work collaboratively within interdisciplinary teams, applying computational expertise to agricultural and plant science research questions.

• PhD in Agriculture, Computing, Data Science, Biological Sciences or a closely related discipline (Associate Level) or currently studying close to completion of PhD (Assistant Level).
• Strong demonstrated knowledge of machine learning methods and their application to data analysis, with relevance to biological, agricultural, or environmental datasets.

• Work in a team and be proactive in preparing publications.
• Contribute to a positive work culture and engage with internal seminars.
• Mentor undergraduate and master’s students working on their projects.

Newcastle University is a great place to work, with excellent benefits. We have a generous holiday package; plus the opportunity to buy more, great pension schemes and a…