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Machine learning techniques for artwork authentication and attribution and accelerator physics

   Department of Mathematical Sciences

  ,  Applications accepted all year round  Awaiting Funding Decision/Possible External Funding

About the Project

This is a multi-disciplinary Ph.D. project that aims to enhance the scope and reliability of machine learning methods applied to the recognition and classification of patterns and images, with applications to artwork classification and attribution, as well as to beam control in accelerators and data analysis in Monte-Carlo simulations. The student will work on the development of efficient multi-classifier neural networks for classifying high-resolution images of the works of art according to their authorship. Another direction of work will be the enhancement of the scope of image analysis by including the hyper-spectral and elemental composition data into the neural network input. The student will have an opportunity to get acquainted with experimental methods such as scanning the works of art with beams from Ion Beam Accelerators, Synchrotron Light Sources, or compact X-ray devices, and with stochastic data analysis methods such as Monte-Carlo techniques. This PhD project is ideal for students interested in Machine Learning and Accelerator Physics or Monte-Carlo methods.

The project will be carried out in collaboration with an industrial partner Art Recognition AG, a startup enterprise in Zurich, Switzerland. This is a unique and exciting project that will apply and develop machine learning techniques both for art attribution and accelerator physics or Monte-Carlo methods. 

We are looking for a candidate with a strong background in applied mathematics, computer science, or theoretical physics, and with excellent coding (preferably Python), data analysis, analytic, and communication skills. Previous experience with Machine Learning and cloud computing would be an advantage.  Applicants are expected to hold, or be about to obtain, a minimum upper second class undergraduate degree (or equivalent) in either Mathematics, Computer Science, or Physics, or a closely related subject. A Masters degree in a relevant subject and/or experience in either applied mathematics, computer science, or numerical methods in physics is desirable.

You will have access to the Cockcroft Institute’s comprehensive postgraduate training in accelerator science, as well as to structured training in data science through our brand-new Liverpool Centre for Doctoral Training for Innovation in Data Intensive Science (LIV.INNO).

Funding and eligibility: UK and international students are eligible to apply. An IELTS score of at least 6.5 is required.

Contact for further information: Pavel Buividovich [] and/or Tessa Charles [].

Anticipated Start Date: October 2022 for 3.5 Years.

To apply for this opportunity, please visit: https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/ and click on the 'Ready to apply? Apply now' button. Please ensure you quote the following reference on your application: Machine learning techniques for attribution of artwork and accelerator physics.

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