Research Associate (Fixed Term)

University of Bath - Mathematical Sciences

Research Associate in Mathematics on Materials for Energy

A UK-funded postdoctoral research position is available at the University of Bath to accelerate Kinetic Monte Carlo Methods for Novel Solar Cell Design. The position is funded by the EU Horizon2020 Project Energy Oriented Centre of Excellence, EoCoE, whose aim is to support numerical modelling of key technologies in sustainable energy sources. Bath is a node in the consortium and involved in the thematic area of "Materials for Energy” www.eocoe.eu/workpackages/materials-energy. This postdoc position will include strong interactions with the other nodes of the EoCoE consortium. 

The aim of the project at Bath is a multiscale study of novel perovskite solar cell materials https://www.electronicsweekly.com/news/research-news/perovskite-materials-solar-expert-speaks-2017-05/ and https://en.wikipedia.org/wiki/Perovskite_solar_cell.

The EoCoE node at Bath is funded by additional EU and EPSRC grants and training networks to look at perovskite and battery materials and consists of the interdisciplinary team Prof Robert Scheichl in the Department of Mathematical Sciences, (www.maths.bath.ac.uk/~masrs), and the computational scientists Prof Alison Walker (http://people.bath.ac.uk/pysabw/), and Prof Saiful Islam (http://people.bath.ac.uk/msi20).

Kinetic Monte Carlo, KMC, methods are required to predict device behaviour from the material properties at the microscopic scale. The postdoc will work with Prof Robert Scheichl on replacing tried and trusted mathematical algorithms employed in KMC by newer methods with better scaling properties. Work funded by EoCoE so far has mainly focussed on atomistic studies of the microscopic processes, such as charge and exciton hopping, recombination rates and light absorption. In the remaining 16 months of the project, using the parameters obtained from the atomistic simulations, work will focus on kinetic Monte Carlo simulations at the mesoscale, computing current-voltage characteristics, charge mobilities and parameters for calculating recombination that can subsequently feed into faster device design offered by continuum models where current-voltage characteristics are obtained.

The kinetic Monte Carlo (KMC) method (http://people.bath.ac.uk/pysabw/abwmod.html, middle column) is closely related to the Gillespie algorithm (https://en.wikipedia.org/wiki/Gillespie_algorithm). Walker is one of the leading researchers in the development and application of KMC. In line with the overarching work package WP1 of EoCoE, this project will focus on ‘reengineering’ the computationally intensive parts of KMC with a view of improving the overall performance and making them more suited to supercomputers with high concurrency. This is likely to involve replacing tried and trusted mathematical algorithms by newer methods with better scaling properties. In particular, we intend to investigate novel multilevel Monte Carlo ideas (http://epubs.siam.org/doi/abs/10.1137/130940761, https://arxiv.org/abs/1409.1838), as well as fast and massively parallel Poisson solvers for the modelling of long-range interactions. 

This is a fixed term position with an anticipated end date of 30 September 2018.

Further details:

The University of Bath is an equal opportunities employer and has an excellent international reputation with staff from over 60 different nations. To achieve our global aspirations, we welcome applicants from all backgrounds.

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