Difference between revisions of "Vacancies"
m (→Vacancies:) |
m (→Vacancies:) |
||
(3 intermediate revisions by the same user not shown) | |||
Line 36: | Line 36: | ||
=== General Purpose Machine Learning Tool-Kit for Bragg Coherent Diffraction Imaging === | === General Purpose Machine Learning Tool-Kit for Bragg Coherent Diffraction Imaging === | ||
− | |||
− | + | Bragg coherent diffraction imaging (BCDI) is a lens-less far field x-ray imaging technique that allows three-dimensional (3D) imaging of quantum materials at the nanometre scale with a sensitivity below a single angstrom. To accomplish this, coherent x-rays from a synchrotron light source are used to illuminate a single nanocrystal which scatters to produce a diffraction (speckle) pattern. That pattern encodes all information about the arrangement of atoms within the nanocrystal. Iterative phase reconstruction computational methods are then routinely used to recover the complex three-dimensional electron density and phase information, which is related to strain in the nanocrystal. | |
+ | |||
+ | Deep learning has emerged as a powerful alternative to the iterative phase retrieval approach, that can provide robust reconstruction of Fourier-space diffraction pattern data where iterative methods often fail to solve the phase retrieval problem. Although emphasis to date has focussed on inversion from Fourier-space to real-space images, the process of recovering real-space images remains unclear due to the inherent and currently intractable complexity of deep learning methods. In this project you will develop Physics-Aware Super-Resolution convolutional neural network tools to enhance the visibility of Fourier-space diffraction patterns thus enabling rapid and accurate reconstruction of phase information. You will build on our recent and significant developments in machine learning (ML) for phase retrieval. You will then apply the newly developed ML tools to study quantum materials at the nanoscale using BCDI. Quantum materials of interest include multiferroics for next generation neuromorphic computing and Li/Na-ion battery cathode materials. | ||
+ | This project is a collaboration with the Ada Lovelace Institute and Diamond Light Source. | ||
+ | |||
+ | Applications are invited online [https://www.southampton.ac.uk/study/postgraduate-research/apply here]. When completing the online form, Select "Programme type: Research", "Academic Year: 2024/25", "Faculty: Faculty of Engineering and Physical Sciences". Then select the "PhD Physics (Full time)" course title. | ||
=== Imaging Quantum Materials with an XFEL === | === Imaging Quantum Materials with an XFEL === | ||
Line 51: | Line 55: | ||
This project is fully funded for 3.5 years, supervised by Dr Marcus Newton and will benefit from access to the European XFEL, Swiss XFEL, SACLA XFEL and PAL XFEL. A background in physics, materials science or inorganic chemistry is desirable but not essential. | This project is fully funded for 3.5 years, supervised by Dr Marcus Newton and will benefit from access to the European XFEL, Swiss XFEL, SACLA XFEL and PAL XFEL. A background in physics, materials science or inorganic chemistry is desirable but not essential. | ||
− | Applications are invited online [https://www.southampton.ac.uk/ | + | Applications are invited online [https://www.southampton.ac.uk/study/postgraduate-research/apply here]. When completing the online form, Select "Programme type: Research", "Academic Year: 2024/25", "Faculty: Faculty of Engineering and Physical Sciences". Then select the "PhD Physics (Full time)" course title. |
Latest revision as of 09:33, 15 October 2024
Vacancies:
General Purpose Machine Learning Tool-Kit for Bragg Coherent Diffraction Imaging
Bragg coherent diffraction imaging (BCDI) is a lens-less far field x-ray imaging technique that allows three-dimensional (3D) imaging of quantum materials at the nanometre scale with a sensitivity below a single angstrom. To accomplish this, coherent x-rays from a synchrotron light source are used to illuminate a single nanocrystal which scatters to produce a diffraction (speckle) pattern. That pattern encodes all information about the arrangement of atoms within the nanocrystal. Iterative phase reconstruction computational methods are then routinely used to recover the complex three-dimensional electron density and phase information, which is related to strain in the nanocrystal.
Deep learning has emerged as a powerful alternative to the iterative phase retrieval approach, that can provide robust reconstruction of Fourier-space diffraction pattern data where iterative methods often fail to solve the phase retrieval problem. Although emphasis to date has focussed on inversion from Fourier-space to real-space images, the process of recovering real-space images remains unclear due to the inherent and currently intractable complexity of deep learning methods. In this project you will develop Physics-Aware Super-Resolution convolutional neural network tools to enhance the visibility of Fourier-space diffraction patterns thus enabling rapid and accurate reconstruction of phase information. You will build on our recent and significant developments in machine learning (ML) for phase retrieval. You will then apply the newly developed ML tools to study quantum materials at the nanoscale using BCDI. Quantum materials of interest include multiferroics for next generation neuromorphic computing and Li/Na-ion battery cathode materials.
This project is a collaboration with the Ada Lovelace Institute and Diamond Light Source.
Applications are invited online here. When completing the online form, Select "Programme type: Research", "Academic Year: 2024/25", "Faculty: Faculty of Engineering and Physical Sciences". Then select the "PhD Physics (Full time)" course title.
Imaging Quantum Materials with an XFEL
Quantum materials can often exhibit novel and multifunctional properties due to strong coupling between lattice, charge, spin and orbital degrees of freedom. When perturbed into an excited state, non-equilibrium phases often emerge on the femtosecond timescale. They include light-induced superconductivity, terahertz-induced ferroelectricity and ultra-fast solid-phase structural transformations. Understanding non-equilibrium phases in quantum materials is of great interest for the development of next generation technologies and to better understand the underlying mechanisms. To further understand these hidden phases, tools to probe quantum materials with femto-second time-resolution are required.
X-ray Free Electron Laser (XFEL) facilities provide ultra-short pulses of coherent x-rays that make it possible to measure ultra-fast dynamics in quantum materials simultaneously with nanoscale spatial resolution and femto-second time resolution. While preliminary work has begun on the use of XFELs to study quantum behaviour in materials, there are a wide range of strongly correlated materials that exhibit novel behaviour that is not well understood.
This project will investigate strongly correlated phenomena in nanoscale quantum materials using time-resolved Bragg coherent diffraction imaging (CDI) at various XFEL facilities. Initial emphasis will reside on the study of structural phase changes in strongly correlated quantum materials such as vanadium dioxide but will continue to expand to other material systems throughout the duration of the project. The overarching goal is to directly observe atomic motions during the event of a quantum phase transition. The ability to quantitatively observe atomic motions within the transition state region where atoms exchange nuclear configurations will greatly facilitate our understanding of the physical processes.
This project is fully funded for 3.5 years, supervised by Dr Marcus Newton and will benefit from access to the European XFEL, Swiss XFEL, SACLA XFEL and PAL XFEL. A background in physics, materials science or inorganic chemistry is desirable but not essential.
Applications are invited online here. When completing the online form, Select "Programme type: Research", "Academic Year: 2024/25", "Faculty: Faculty of Engineering and Physical Sciences". Then select the "PhD Physics (Full time)" course title.