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Dr Barry Dillon,
Dr William Smyth,
Dr Muskaan Singh
Monday, February 24, 2025
Competition Funded PhD Project (Students Worldwide)
About the Project
Machine-learning algorithms are becoming essential components of scientific analyses, in particular in particle physics, where current analyses at the Large Hadron Collider (LHC) are beginning to use machine-learning tools to push the boundaries of what we can learn about the fundamental laws of nature. In this PhD project the student will develop new machine-learning tools to improve our understanding of the processes being measured at the LHC, and in turn aide in the search for new physics. The Intelligent Systems Research Centre (ISRC) at Ulster University has a rich program of research in various areas of machine-learning, and the research focus of the main supervisor for this project is in machine-learning for LHC phenomenology, anomaly detection, and in efficient neural network architectures. In general, we encourage the study and incorporation of new ideas emerging in the field of machine-learning for applications in physics. The research also has applications more broadly in the field of physics and other sciences. We invite applications from candidates with a background in physics, computer science, mathematics, or related disciplines.
References
1. Modern Machine Learning for LHC Physicists (lecture notes)
https://arxiv.org/abs/2211.01421
T. Plehn, A. Butter, B. Dillon, T. Heimel, C. Krause, R. Winterhalder
2. Better Latent Spaces for Better Autoencoders
https://arxiv.org/abs/2104.08291
B Dillon, T. Plehn, C. Sauer, P. Sorrenson
3. A normalized autoencoder for LHC triggers
https://arxiv.org/abs/2206.14225
B. Dillon, L. Favaro, T. Plehn, P. Sorrenson, M. Krämer
4. Symmetries, safety, and self-supervision
https://arxiv.org/abs/2108.04253
B. Dillon, G. Kasieczka, H. Olischlager, T. Plehn, P. Sorrenson, L. Vogel
5. Uncovering latent jet substructure
https://arxiv.org/abs/1904.04200
B. Dillon, D. A. Faroughy, J. F. Kamenik
https://arxiv.org/abs/2211.01421
T. Plehn, A. Butter, B. Dillon, T. Heimel, C. Krause, R. Winterhalder
2. Better Latent Spaces for Better Autoencoders
https://arxiv.org/abs/2104.08291
B Dillon, T. Plehn, C. Sauer, P. Sorrenson
3. A normalized autoencoder for LHC triggers
https://arxiv.org/abs/2206.14225
B. Dillon, L. Favaro, T. Plehn, P. Sorrenson, M. Krämer
4. Symmetries, safety, and self-supervision
https://arxiv.org/abs/2108.04253
B. Dillon, G. Kasieczka, H. Olischlager, T. Plehn, P. Sorrenson, L. Vogel
5. Uncovering latent jet substructure
https://arxiv.org/abs/1904.04200
B. Dillon, D. A. Faroughy, J. F. Kamenik
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