PhD position to build and apply anomaly detection methods with CMS

Building Robust and Calibrated Generative Models to Detect Anomalies in Data

Supervisors: Prof. Gregor Kasieczka (UHH), Prof. Timo Gerkmann (UHH)

Despite an impressive and extensive effort by the Large Hadron Collider (LHC) collaborations at CERN, currently, there is no convincing evidence for new particles produced in high-energy collisions. However, the Standard Model cannot be the final theory of nature. Past years have seen an enormous increase in anomaly-based strategies to search for new physics, such as the weakly supervised CATHODE approach co-developed in Hamburg. A key ingredient in this approach is training a generative model to learn an in-situ model of the background data.

This project will combine state-of-the-art techniques in quantifying the uncertainty of generative models and apply them to improve anomaly detection capabilities in particle physics to aid the potential discovery of new fundamental particles.

Requirements:

• Master's degree in computer science or physics
• proficient in Python and familiar with modern machine learning libraries
• desirable: experience with generative machine learning models, anomaly detection techniques, or particle physics analysis pipelines