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#PhD Studentship - Inverse design and machine learning techniques applied to thermoelectric nanomaterials

PhD Studentship:

Inverse design and machine learning techniques applied to thermoelectric nanomaterials

School of Engineering, University of Warwick

Start date:  By arrangement

Duration:  3 years

The project:

Thermoelectric materials convert directly heat into electricity, and they are very promising in contributing towards energy savings, reduction of the use of fossil fuels, powering autonomous sensors, etc. Nanostructured thermoelectric materials have recently emerged as an advanced technology with much higher performance and lower prices compared to existing bulk materials. The design and optimization of nanostructures, however, is not a trivial task, and involves understanding electronic and phonon transport phenomena at the nanoscale, such as ballistic to diffusive and quantum mechanical to the semiclassical crossovers. In addition, the atomistic details of nanostructures, the details of the geometry, and the details of different material compositions determine the thermoelectric properties at large. Furthermore, the parameters that optimize thermoelectric transport are adverse interrelated, and optimizing one parameter usually negatively affects another.

This PhD project creates and utilizes inverse design machine learning techniques to assist the design of next generation nanostructured thermoelectric materials. It will be aligned with a larger effort within a European Research Council (ERC) funded project and will use electrothermal transport tools that will be developed within that project. It will provide automated performance optimization, achieved through the use of the Markov Chain Monte Carlo (MCMC) technique to nanostructured materials. This will be used within the Adaptive Sequential Monte Carlo (ASMC) scheme, which can provide parallelisation in the optimization process. To speed up the optimization procedure, a well calibrated Gaussian Process (GP) surrogate will be used to emulate the functionality of existing electrothermal simulators, and then the surrogate will be used within the ASMC algorithm.


Due to funding restrictions this award is available for well-qualified UK or EU students (oversees students can apply, but need to meet the difference in costs).


The annual stipend will be £14,254 (tax free), for 3 years, with all tuition fees paid at the UK/EU rate.

How to Apply

To apply for this post you must complete the online application form and quote scholarship reference NNThermo

As soon as you have a University ID number you will be invited to upload your degree certificate, transcripts, CV and a personal statement that explains your specific research interests and why you should be considered for this award. Full details of the admission requirements are available at:

Application Form Course Details:

Department: School of Engineering

Course Type: Research

Course: Engineering (PhD)

Application form: