- Take a leading role in those aspects of the project which relate to establishing relationships between fluvial water/ sediment dynamics and the regional physical drivers of change.
- Write open-source scripts in Python or R to retrieve, manage, prepare, analyse, and visualise data from a variety of sources, including satellite imagery and records of directly monitored rates of sediment and water discharge.
- Apply machine learning and/or Bayesian Modelling techniques to establish links between drivers and suspended sediment fluxes.
- Adapt existing and develop new research methodologies and materials.
- Co-ordinate and collaborate with colleagues in partner organisations.
- Lead the preparation of research publications for submission to peer-reviewed academic journals based on the research undertaken.
- Present papers at conferences, public meetings or seminars.
- Contribute to the planning and organising of the research programme and project data.
- Hold, or be close to completion of a relevant PhD*/DPhil, together with experience or interest in physical geography, geomorphology, hydrology or equivalent
- Possess strong GIS, large dataset manipulation and coding skills, preferably using Python or R
- Experience of applying machine learning and/or Bayesian Modelling techniques
- Possess experience downloading, extracting, and manipulating/analysing data from a diverse range of data sources using GIS or equivalent, including satellite imagery and/or using Google Earth Engine
- Be able to manage own academic research and associated activities
- Possess excellent oral and written communication skills
Research Fellow in Drivers of Global Water and Sediment Dynamics - Southampton, United Kingdom - University of Southampton
Description
The post holder will undertake scientific research on global river and sediment dynamics in support of the UKRI funded research project, GLOSS (GLObal Suspended Sediment: drivers, trends and future trajectories). The researcher will work with a global sediment database and use remotely sensed and other geographical data with machine learning/Bayesian Modelling techniques to establish drivers of global sediment flux. They will use numerical models and statistical processing techniques to establish patterns and future trajectories of global change.
The post holder will be expected to be able to locate, curate and manipulate time-series global physical geographical attribute datasets of relevance to river sediment dynamics (e.g. DEMs, precipitation, land use/cover, reservoirs, population dynamics etc.) using GIS and bespoke code. These datasets will be used in conjunction with new estimates of global river suspended sediment fluxes to establish the physical drivers of sediment variability, using machine learning and Bayesian Modelling techniques. This post is for 18 months and is based at University of Southampton.
The School of Geography and Environmental Science is committed to fostering a culture of equality, diversity and inclusion. We welcome applications from suitably qualified candidates, and particularly encourage applications from candidates belonging to groups who are under-represented within academic posts at the University, including, but not limited to: people from Black and minority ethnic groups; and disabled people. The School is committed to providing equal opportunities for all and a range of family friendly policies, and we encourage staff to apply for flexible working. We are a Disability Confident employer and the School holds a bronze Athena SWAN award. We are a signatory to the Concordat to Support the Career Development of Researchers and can provide opportunities for career development and mentoring.
Equality, Diversity and Inclusion | University of Southampton
Responsibilities
Selection criteria
Applications will be considered from candidates who are working towards or nearing completion of a relevant PhD qualification. Prior to the qualification being awarded the title of Senior Research Assistant will be given.
£34,980 to £42,978 per annum