UKRI CDT PhD Scholarship in Artificial Intelligence 2021

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UKRI CDT Scholarship in Artificial Intelligence 2021

UKRI CDT PhD Scholarship in Artificial Intelligence 2021

Scholarship Type: Phd

UKRI CDT Scholarship in Artificial Intelligence, Machine Learning and Advanced Computing: Machine learning for multidimensional ultrafast X-ray spectroscopy

This scholarship is funded by UK Research and Innovation (UKRI).

The UK Research and Innovation (UKRI) Centre for Doctoral Training (CDT) in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society.

Our doctoral training programme is constructed around three research themes:

  • T1: data from large science facilities (particle physics, astronomy, cosmology)
  • T2: biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics)
  • T3: novel mathematical, physical, and computer science approaches (data, hardware, software, algorithms)

Supervisors:

  • First supervisor: Dr Kevin O’Keeffe
  • Second supervisor: Dr Adam Wyatt

Research theme:

  • T1: data from large science facilities
  • T3: novel mathematical, physical and computer science approaches

Project description:

Observing the dynamics of molecular systems on their natural timescale is a fundamental challenge in physics and chemistry. Recently, multidimensional spectroscopy using ultrafast x-ray pulses has emerged as a powerful method for tracking the motion of electrons during the first few femtoseconds of a light-atom interaction. This technique records the spatially and spectrally-resolved interference pattern from two laser-generated x-ray sources at multiple source positions, providing access to phase information crucial for resolving ultrafast dynamics. Although this technique enables measurements with unprecedented temporal stability, the 4-dimensional interferograms which are generated are highly structured and challenging to analyse. The primary goal of this project will be to develop a machine learning tool capable of reliably identifying the key signatures in the interferogram related to electronic motion in atomic systems such as argon. The algorithm will be trained using simulated interferograms based on strong-field calculations before being implemented on real data sets. The algorithm will then be extended to the analysis of interferograms generated using more complex targets such as molecular nitrogen and carbon dioxide. Developing robust methods for extracting data from such interferograms will provide new opportunities for understanding the behaviour of bond formation and breaking at the natural timescale of chemical reactions.

Host Institution/Country:

Swansea University and Central Laser Facility, Wales.


Scholarship Eligibility:

  • The typical academic requirement is a minimum of a 2:1 undergraduate degree in biological and health sciences; mathematics and computer science; physics and astronomy or a relevant discipline.
  • Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software (or willingness to learn it).
  • This scholarship is open to UK and international candidates (including EU and EEA).


Scholarship Value:

This scholarship covers the full cost of tuition fees and an annual UKRI standard stipend (currently £15,285 for 2020/21).

Additional funding is available for training, research and conference expenses.

How to Apply:

Please visit our website for more information.

Application Deadline:12 February 2021

Start date: October 2021

Apply Now

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