Over the summer of 2020 we hosted our very first UKRI Research Experience Placements (REPs). This fully funded placement scheme is intended to encourage undergraduate students studying quantitative subjects such as maths, engineering and computer science – subjects outside the typical NERC remit – to gain practical experience and consider a career in environmental science.
During REPs placements, a number of undergraduate students join the partner schools of the DTP (biological sciences and geosciences) for 6-10 weeks over the summer vacation, to undertake a short-term research project. The aim is to develop multi-disciplinary projects and for these students to see how their quantitative skills can be applied effectively within the environmental sciences. The scheme also fosters relationships between schools and disciplines, an area many of our applicants wish to see more of!
What counts as a quantitative subject? See the below list:
- Computer Science
We were lucky enough to receive UKRI funding to support 5 outstanding undergraduates over the summer of 2020, all of which have been exceptionally successful! We advertised a selection of 9 projects across the 4 partner schools, and received over 90 applications! Placement typically take place between June and September. All projects were designed specifically for remote delivery in 2020.
See below for details of our 2020 placement students and their projects. You can find our more about the REPs scheme here, and read about our 2020 REPs student experiences below and here. We expect to run this scheme on an annual basis so watch this space! The scheme is likely to be launched around March 2021 for placements taking place over the summer of 2021.
Milena Zagulak, MEng Mechanical Engineering, University of Aberdeen
Assessing the long-term impact of climatic variables on coastal changes in Scotland using geospatial tools
Emily Gribbin, MSci Mathematics, Queen’s University Belfast
Climate change and the future viability of Europe’s longest Ice Road
Marcell Veiner, BSc Mathematics & Computing Science, University of Aberdeen
Implementing a Machine Learning Approach for the understanding of social learning in honeybee foragers
Read Marcell’s blog post here.
Chiara Ferdynus, MEng Electrical and Electronics Engineering, University of Aberdeen
Modelling historical ice phenology data and implications for future environmental change
Scott Angus, BSc Physics, University of Aberdeen
Geostatistical analysis of spatiotemporal trends of COVID-19 spread in UK