PhD Studentship in Machine Learning for Inverse Design of Materials for Skyrmionics

University of Southampton School of Engineering

United Kingdom

 

PhD Studentship - Machine learning for inverse design of materials for skyrmionics


School of Engineering
Location: Highfield Campus
Closing Date: Monday 31 August 2020
Reference: 1246720DA
Supervisor: Dr Ondrej Hovorka

Co-supervisor Prof Hans Fangohr

Project description

Skyrmionics is emerging as one of the most significant areas in magnetism with prospects to shape the research over the next few decades. It offers the potential for developing novel low-cost, energy-efficient information storage and processing technologies, or magneto-electronic sensors and devices. Fundamentally, skyrmions are excitations of matter, whose occurrence and collective properties remain only partially understood [1]. This complicates the discovery of new materials capable of sustaining skyrmion phases that could be suitable for room-temperature device applications. The traditional “direct materials design” approach based on searching for novel compounds through extensive explorations of the chemical and physical parameter space remains challenging. This leads to the pursuit of the so-called “inverse design” to accelerate the discovery of new materials, closely integrating the machine learning tools, computational modeling, and experiments [2].

The aim of this PhD project is to devise inverse design strategies for identifying materials with target functionalities suitable for skyrmionics. We plan to adopt methodology combining machine learning, computational modelling based on a broad class of classical spin and micromagnetic models, and experimental material characterization data. Our group within the Faculty of Engineering and Physical Sciences at the University of Southampton is renowned for data science and computational modelling, and has a long-term expertise in computational magnetism. The access to experimental data will be facilitated through close collaboration with our project partners within the UK’s National EPSRC Skyrmion project, which involves partners from Durham University, University of Southampton, University of Cambridge, University of Oxford, and Warwick University.

We seek highly motivated graduates in Physics, Engineering or Mathematics with a high degree of computer programming proficiency and strong mathematical skills. Basic familiarity with computational magnetism (e.g. spin Monte-Carlo methods, micromagnetics) and standard machine learning software libraries (e.g. Python’s Scikit-Learn) is desirable but not essential.

References:

[1] T. Lancaster, Skyrmions in magnetic materials. Contemporary Physics 60, 246–261, (2019).

[2] A. Zunger, Inverse design in search of materials with target functionalities. Nat. Rev. Chem. 2, 0121 (2018).

If you wish to discuss any details of the project informally, please contact Ondrej Hovorka, Email: o.hovorka@soton.ac.uk, Tel: +44 (0) 23 8059 4898.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: applications should be received no later than 31 August 2020 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: full tuition fees for EU/UK students plus for UK students, an enhanced stipend of £15,285 tax-free per annum for up to 3.5 years.

How To Apply

Applications should be made online, please select the academic session 2020-21 “PhD Eng & Env (Full time)” as the programme. Please enter Dr Ondrej Hovorka under the proposed supervisor.

Applications should include:

Curriculum Vitae

Two reference letters

Degree Transcripts to date

Apply online:

For further information please contact: feps-pgr-apply@soton.ac.uk


In your application, please refer to Polytechnicpositions.com

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