|Title:||PhD studentship on future accelerator technologies|
The search for new physics beyond the standard model has so far yielded no evidence and experimental particle physicists are preparing for the next generation of discovery machines. Taking advantage of these machines requires novel developments both in the discovery approach and in the detector technology supporting such searches.
The aim of this research project will be primarily investigating the use of Machine Learning techniques implemented on commercial FPGA accelerators to address the data processing and acquisition challenges of detectors at the next generation of particle physics experiments.
All future accelerator scenarios (ranging from “classical” high-energy proton-proton and electron-positron machines to the extraordinary paradigm shift of muon colliders) will in most cases swamp the next generation of particle detectors with extraordinary amounts of charged particles. A central use case of the novel data processing techniques will be the reconstruction of particle trajectories in difficult environments: a pivotal challenge which well fits the emerging programmable technologies for computing acceleration.
A variety of approaches will be possible: from the application to present experiments (e.g. ATLAS) to the study of extreme collision environments such as HL-LHC, FCC and muon colliders. A fraction of the project will be devoted to the study of the physics potential of such environments, e.g. in the investigation of new phenomena related to Lepton Flavour Universality violation, with the possibility of employing present and future ATLAS detector data in an initial measurement and/or proof of principle.
For more details about the project, please contact Prof. Alessandro Cerri: A.Cerri@sussex.ac.uk).
For practical questions about applications and/or eligibility for funding, please contact firstname.lastname@example.org
For academic questions please contact the coordinator of EPP PhD admissions, Dr. W. Clark Griffith: W.C.Griffith@sussex.ac.uk
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