The following projects are part of this work package:
Open positions for start in 2024
- Efficient and accurate Technology Computer-Aided Design simulations with machine learning and their application to develop monolithic CMOS sensors for physics experiments
- Machine learning methods for numerical estimates of transport coefficients of quark-gluon plasma and the classification of urban landscapes
- Digital Twin Model of the Milky Way
- Machine Learning methods to identify faint stellar streams in Milky Way-type galaxies
- Optical fiber-based RF-breakdown detection and prediction
- Developing Machine Learning methods to constrain the properties of the Quark-Gluon Plasma
2023
- How do supermassive black holes affect their host galaxies?
- Machine learning methods for generation of random images and equilibrated configurations of gluon fields in Quantum Chromodynamics
- Using neural networks and clustering algorithms to understand the mass flows and energy cycles at the heart of our Galaxy
2022
- Reconstructing the assembly history of our Galaxy using neural networks
- Constraining the complex relationship between galaxies and their dark matter haloes with machine learning
- Artificial Intelligence to improve HPGe detector performance and reliability
- AI: from high energy physics to medical applications
Back to: Centre for Doctoral Training for Innovation in Data Intensive Science