Research Fellow In Machine Learning
University Of Southampton
Research Fellow in Machine Learning Environmental Change & Sustainability
Location: Highfield Campus Salary: £31,406 to £33,309 per annum Full Time Fixed Term (until 31/03/2022) Closing Date: Monday 06 September 2021 Interview Date: To be confirmed Reference: 1476421WR
University of Southampton, School of Geography and Environmental Science, Post-Doctoral Research Fellow in Machine Learning Methods for Crop Forecasting (Machine Learning, Crop/Hydrological Modelling and Forecasting, Water Resources and Agricultural Management, Water Security, Sustainability).
The University of Southampton seeks a data analyst or environmental modeller with experience in the above areas for a 7-month research position. You will be part of a team on an ongoing project “A new paradigm in precision agriculture: assimilation of ultra-fine resolution data into a crop-yield forecasting model”. The project is focused on the nexus of water and food security and is a collaboration between the University of Southampton and King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. You will be working directly with Professor Justin Sheffield and joining other post-docs and PhDs based in Southampton and KAUST.
The project’s goal is to improve the understanding and management of agricultural systems in dryland environments via the development and use of new remote sensing technologies, assimilation approaches and forecasting methods. Specifically, the project is focused on identifying the critical processes and constraints on dryland farming, how to simulate and understand these, and how to integrate models with advanced observation and management strategies to deliver a transformative decision making framework.
We seek candidates with a PhD in Geography, Engineering, Environmental Science, Mathematics, or Computing Science or a related discipline, and with excellent data analysis and computational skills with experience in applying machine learning methods for environmental prediction. Candidates will preferably have an understanding and experience in hydrology and agricultural forecasting (hydroclimate data analysis, hydrological and crop modelling, hydrological and crop production forecasting, data assimilation, data visualization), to undertake research on characterising and understanding the variability and predictability of water resources and crop health/production across scales in dryland environments, focusing on case studies in Zambia and the U.S. You will develop and apply data-driven machine learning methods to predict crop dynamics and yields based on large datasets of observations and model outputs. The work will contribute to the development of seamless monitoring and forecasting systems with improved predictability and reduced uncertainty via bias correction, data assimilation and model merging, and optimization of management strategies to improve crop production.
You will have knowledge and experience of working with statistical and machine learning methods, and large datasets in a Linux/Unix environment, including scientific programming skills in Python, R, C, Fortran or similar, and using these to address environmental problems. Experience with Geographic Information Systems (GIS) is desirable. Previous experience of working on agricultural/hydrological problems in dryland regions, and a knowledge of water and food security challenges, are both desirable, but not required.
You will work closely with an interdisciplinary team of researchers and students at Southampton and KAUST to develop and carry out research, write research reports and journal papers. There will be opportunities to develop knowledge and professional skills in multi-disciplinary research projects and international development issues.
Further details and informal enquiries can be made to Prof. Justin Sheffield (email@example.com)
Applications for Research Fellow positions will be considered from candidates who are working towards or nearing completion of a relevant PhD qualification. The title of Research Fellow will be applied upon successful completion of the PhD. Prior to the qualification being awarded the title of Senior Research Assistant will be given.
You should submit your completed online application form at https: // jobs.soton.ac.uk. The application deadline will be midnight on the closing date stated above. If you need any assistance, please call Kate Pounds (Recruitment Team) on +44 (0) 23 8059 5456 or email firstname.lastname@example.org Please quote reference 1476421WR on all correspondence.
Job Description and Person Specification
We aim to be an equal opportunities employer and welcome applications from all sections of the community. Please note that applications from agencies will not be accepted unless indicated in the job advert.