A PhD position to work on the analysis of ALICE data and on the application of modern machine learning techniques to High Energy Physics is available at the Department of Electronics, Mathematics and Computing of the University of Derby, UK.
The official call can be found here: https://www.derby.ac.uk/research/degrees/apply/studentship-opportunity/engineeringandtechnology/e&tstudentships/
For information on potential HEP and ALICE–related projects, please contact Michele Floris (michele.floris@cern.ch, https://cern.ch/mfloris).
In particular, two lines of research are being developed:
1. The analysis of heavy-flavour hadrons and QCD jets provide crucial information on the microscopic structure of the Quark-Gluon Plasma created in heavy-ion collisions. The measurement and identification (tagging) of these observable is experimentally challenging, because of the large background present in heavy-ion data. The student will work on the analysis of data collected by the ALICE experiment, in particular contributing to develop new (deep) machine-learning methods, expected to significantly enhance the existing experimental capabilities.
2. Detector simulation is one of the most CPU-intensive tasks in high-energy physics. A reduction of the simulation time will be crucial for the future high-luminosity runs at the LHC. Recent pioneering studies have demonstrated that machine-learning models (in particular based on Generative Adversarial Networks) can successfully approximate full Monte-Carlo simulations for simple detector geometries, but significant additional research and development work is needed to fully demonstrate the viability of this approach and to implement general-purpose tools.
The successful candidate will be able to contribute to one of these lines of research.
The official call can be found here: https://www.derby.ac.uk/research/degrees/apply/studentship-opportunity/engineeringandtechnology/e&tstudentships/
For information on potential HEP and ALICE–related projects, please contact Michele Floris (michele.floris@cern.ch, https://cern.ch/mfloris).
In particular, two lines of research are being developed:
1. The analysis of heavy-flavour hadrons and QCD jets provide crucial information on the microscopic structure of the Quark-Gluon Plasma created in heavy-ion collisions. The measurement and identification (tagging) of these observable is experimentally challenging, because of the large background present in heavy-ion data. The student will work on the analysis of data collected by the ALICE experiment, in particular contributing to develop new (deep) machine-learning methods, expected to significantly enhance the existing experimental capabilities.
2. Detector simulation is one of the most CPU-intensive tasks in high-energy physics. A reduction of the simulation time will be crucial for the future high-luminosity runs at the LHC. Recent pioneering studies have demonstrated that machine-learning models (in particular based on Generative Adversarial Networks) can successfully approximate full Monte-Carlo simulations for simple detector geometries, but significant additional research and development work is needed to fully demonstrate the viability of this approach and to implement general-purpose tools.
The successful candidate will be able to contribute to one of these lines of research.