Project description: This project will take advantage of numerous observations of different solar activities including CMEs, flares and solar cycles, and employing the modern machine learning algorithms, in particular, Support Vector Machine algorithm (SVM) and time-dependent ML algorithm (e.g. unsupervised cGAN), to make fast and accurate predictions of the above solar activities.
More information of the project: Prof. Robert von Fay-Siebenburgen (r.von.fay-siebenburgen (a) sheffield.ac.uk)
Host: University of Sheffield, UK.
Secondment Host: Università degli studi di roma Tor Vergata, Italy.
Industrial Training: Hungarian Solar Physics Foundation, Gyula Bay Zoltán Solar Observatory, Gyula, Hungary (solar observations, 1 month), PTECH (2 months; software development and design).
Project specific requirements
Mobility rule: The applicant must not have resided or carried out their main activity (work, studies, etc.) in UK for more than 12 months in the 3 years immediately before the call deadline ( see details for the MSCA mobility rule here).
Preferred skills: Good programming skills
Desired Skills: Experience with several of the following programming languages / packages: Python, IDL, C, LaTeX. Experience in AI and/or Machine Learning, knowledge of solar and space weather physics.
Host degree requirements: MSc degree or equivalent in applied maths or in a related field (primarily physics or astronomy; other science or engineering degrees may be marginally admissible) with a grade of about 70 % or above. Applicants still pursuing their MSc studies should obtain their degree before starting. The candidate has to be officially admitted to PhD studies at Sheffield University. For more detailed information about the admission process see here.
Secondment host degree requirements: MSc (120 ECTS) or equivalent degree in Physics or Astronomy (and related fields)
Host language requirement: read requirements from here.
Secondment host language requirements: English