Details of Award
NERC Reference : NE/M006328/1
Provision of Continuing Professional Development in mathematical modelling and statistics for NERC doctoral students
Training Grant Award
- Lead Supervisor:
- Professor M Heath, University of Strathclyde, Mathematics and Statistics
- Grant held at:
- University of Strathclyde, Mathematics and Statistics
- Science Area:
- Atmospheric
- Earth
- Freshwater
- Marine
- Terrestrial
- Overall Classification:
- Atmospheric
- ENRIs:
- Biodiversity
- Environmental Risks and Hazards
- Global Change
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Earth & environmental
- Complexity Science
- Population Ecology
- Ecosystem Scale Processes
- Ecosystem Scale Processes
- Abstract:
- Our programme will draw on multidisciplinary expertise in the Strathclyde Department of Mathematics and Statistics to deliver 3 doctoral training courses addressing the NERC Headline Skills gap in mathematical modelling and statistics. The courses will build on the successful courses which ran for the first time in 2014 supported by the pilot NERC Postgraduate and Professional Skills Development Awards. Based on the extremely positive feedback for these courses, we anticipate high demand from within the NERC doctoral student community. One particularly well-received aspect of the courses was the opportunity for students to consider their own research areas in light of knowledge gained, and therefore we propose to extend this part of the courses, extending the duration of each course from 3 to 4 days. This will further assist in the key goal of each course, to provide students with transferable skills that they can apply to their research. The courses will each use example data sets and models, but, as before, students will be encouraged to bring their own data and models to discuss with co-trainees and tutors. Course 1: ODEs in environmental science: Build your own models in R. This course will 'lift the lid' on ordinary differential equations (ODEs) used to model processes in the environmental sciences. The emphasis will be on careful model formulation and practical numerical implementations. Drawing from a range of examples from simple predator-prey interactions, through to physical environmental models and realistic ecosystem models, we will show students how to formulate from scratch problems in terms of differential equations, as well as how to code and solve them in the opensource, well supported, and widely used programming environment R. A key part of this course will be an opportunity for all students to bring along their own projects and data and have the course leaders lead them through developing their own research story-line into differential equations, and then into working R code. This 'workshop' aspect of the course was piloted in 2014 and it was very highly valued by the students. A number of the students have remained in contact, and we continue to assist them in their model development. Course 2: Bayesian methods to fit statistical models in environmental science. Using examples from environmental science throughout, Bayesian model fitting methods will be introduced and compared to classical approaches. We will demonstrate how to set up complex structural models where Bayesian methods are necessary. Extensions to spatial and temporal smoothing, for example in disease mapping, will also be covered. The OpenBUGS statistical package will be used in conjunction with R for practical sessions. The focus will be on applications and modelling, particularly in areas cognisant with the students' research, but will also cover sufficient theory to explain the modelling concepts. We assume some previous exposure to elementary statistics. Course 3. Modelling structure and dynamics in complex networks. The study of complex networks has become an important interdisciplinary field of research in 21st century, with large and increasing impacts in biology, society, technology and ecology. The aim is to deliver statistical-mechanics approaches to understand network organisation and function. This involves properties such as expansibility, topological and functional bottlenecks, organisation of clusters, global communicability, "clumpiness" of nodes in a network, and returnability. In this course the basic concepts of network theory are introduced, such as small-worldness and scale-freeness. The study of node centrality for networks and its relevance for biological and ecological networks is also studied. Finally, we will explore models for analysing dynamical processes, such as synchronization, epidemic spreading and replication-mutation, and others from the students' own research fields.
- NERC Reference:
- NE/M006328/1
- Grant Stage:
- Completed
- Scheme:
- Doctoral Training
- Grant Status:
- Closed
- Programme:
- Advanced Training
This training grant award has a total value of £49,589
FDAB - Financial Details (Award breakdown by headings)
| Total - Other Costs |
|---|
| £49,589 |
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