Thesis supervisor: László Dobos
Location of studies (in Hungarian): ELTE TTK Komplex Rendszerek Fizikája Tanszék Abbreviation of location of studies: ELTE
Description of the research topic:
Astronomy is one of the first and leading field where large amount of data is produced by the large observing instruments. Th e large amount of data, and the complexity of questions require the use of advanced computational techniques. Several machine learning methods has been already used to analyse astronomical observational data, like random forests or support vector machines. Neural networks, especially deep learning seems to be very efficient with the use of multi-core architectures, like GPUs. During the PhD research, the candidate will learn to use these methods and analyse various astronomical (mainly extragalactic) datasets. Depending on the background and interest of the candidate, (s)he can investigate questions like evolution of galaxies or determine the large scale structure of the Universe with these tools, or dive deeper into the theoretical understanding and development of machine learning algorithms.
Required language skills: English Further requirements: Reading articles, take part in discussions, write scientific papers, present at conferences in English. Backgrouns in physics, astronomy, computer science.
Number of students who can be accepted: 1
Deadline for application: 2018-05-31
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
Login
