{"id":294917,"date":"2024-04-19T14:41:06","date_gmt":"2024-04-19T14:41:06","guid":{"rendered":"https:\/\/southafricaportal.com\/?p=294917"},"modified":"2024-04-19T14:41:06","modified_gmt":"2024-04-19T14:41:06","slug":"scholarship-in-bayesian-analysis-university-of-cambridge","status":"publish","type":"post","link":"https:\/\/southafricaportal.com\/scholarship-in-bayesian-analysis-university-of-cambridge\/","title":{"rendered":"How to Apply For Scholarship in Bayesian Analysis, University of Cambridge 2024\/2025"},"content":{"rendered":"

A full-time PhD position funded by UKRI is available for 36 months in the first instance within the context of the ERC doctoral network GLITTER. The project focuses on the development of modern antenna technology and radio instrumentation for GNSS-R and Radio Astronomy from space. The successful candidate will join a multidisciplinary team, benefit from various training events, and work closely with experts from PolyChord Ltd and the University of Cambridge\u2019s Kavli Institute for Cosmology.<\/p>\n

SCHOLARSHIP AT A GLANCE:<\/strong><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n
Type<\/strong><\/td>\nPhD<\/td>\n<\/tr>\n
Deadline<\/strong><\/td>\n18 May, 2024\/2025<\/td>\n<\/tr>\n
Location<\/strong><\/td>\nUnited Kingdom<\/td>\n<\/tr>\n
Eligible Countries<\/strong><\/td>\nInternational Students (All Countries)<\/td>\n<\/tr>\n
Number of Award<\/strong><\/td>\nNA<\/td>\n<\/tr>\n
Value of Award<\/strong><\/td>\nFull Scholarship<\/td>\n<\/tr>\n
Duration of Award<\/strong><\/td>\nNA<\/td>\n<\/tr>\n
Eligible Field of Study<\/strong><\/td>\nBayesian Analysis<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

Description For\u00a0<\/strong>2024\/2025 Scholarship in Bayesian Analysis, University of Cambridge<\/h2>\n

The project aims to revolutionize satellite configuration and data processing using Bayesian analysis and machine learning techniques. The PhD candidate will develop analysis tools for satellite earth observation, focusing on GNSS-R. Collaborating with experts from academia and industry, the candidate will explore optimization algorithms, Bayesian parameter estimation, likelihood-free inference techniques, and machine learning emulators.<\/p>\n

Eligibility For\u00a0<\/strong>2024\/2025 Scholarship in Bayesian Analysis, University of Cambridge<\/h2>\n