Centre for Environmental Mathematics

The Centre for Environmental Mathematics leads four MSc programmes on Applied Data Science for addressing Grand Challenges. These programmes are aimed at those looking to develop and apply modern techniques from data science and artificial intelligence in an interdisciplinary context, with a focus on energy, health, environment and conservation.

Find out more about MSc Applied Data Science and Modelling

Find out more about MSc Applied Data Science (Environment and Sustainability)

Find out more about MSc Applied Data Science (Renewable Energy)

Find out more about MSc Applied Data Science (Ecology and Evolution)

Applicants are encouraged equally from the life, social and environmental sciences (including those who may not have much experience in scientific computing and mathematical modelling) and the engineering, maths and physical sciences. This diverse cohort of students helps foster the important interdisciplinarity.

The programmes employ research-informed, challenge-led and solution-focussed learning in collaboration with internationally renowned researchers at the Environment and Sustainability Institute, Renewable Energy Group, and Centre for Ecology and Conservation at the University’s Penryn Campus. There are four themes, described in more detail below: Environment and Sustainability, Modelling, Renewable Energy and Ecology and Evolution.

We are always open to collaboration with individuals and institutions with interests overlapping with our group members. If you have an opportunity you would like to discuss, then please get in touch with our members directly following the links to their profiles within the 'research and impact' or 'people' webpages.

Postgraduate research opportunities

Please get in touch if you would like to pursue a PhD with the group. Below is a list of indicative titles with the relevant contact. We are also open to suggestions of other topics and to cocreating research proposals, please examine the profiles of our group members and contact the person whose interests lie closest to your own.

• “Optimal design of mechanical controllers” (Tim Hughes)
• “Exploiting physical certainties in robust control” (Tim Hughes)
• “Control theoretic paradigms in an equation based modelling framework” (Tim Hughes)
• “Mathematical modelling of the shallow-water equations with temperature gradients” (Hamid Alemi Ardakani)
• “Structure-preserving numerical discretisation for Hamiltonian partial differential equations” (Hamid Alemi Ardakani)
• “Mathematical modelling of the coupled fluid-body dynamics using the Hamiltonian Particle Mesh theory” (Hamid Alemi Ardakani)
• "Bayesian Deep Learning and Sampling Methods for Probabilistic Seismic Inversion and Imaging" (Saptarshi Das)
• “Multi-agent reinforcement learning control for renewable energy integration in smart grids with economic load dispatch” (Saptarshi Das)
• “GIS data-driven intelligent energy storage placement decisions based on renewable energy capacity patterns” (Saptarshi Das)
• “Joint State/Parameter Estimation, Uncertainty Modelling and Bayesian Model Selection in Smart Grid Signal Processing” (Saptarshi Das)
• “Cyber-physical Systems Modelling and Networked Control of Smart Grids with More Renewable Energy Integration” (Saptarshi Das)
• “Robust control designs for integrated renewable energy systems” (Markus Mueller)
• “Adaptive power management strategies for arrays of oscillating water column wave energy converters” (Markus Mueller)
• “Modelling host-microbe interactions as evolutionary drivers of virulence” (Mario Recker)
• “Investigating the complex ecology of multi-species, multi-host disease systems” (Mario Recker)
• “Feedback loops, selection pressures and anti-microbial resistance" (Stuart Townley)