Two Postdoc positions in Machine Learning and Elementary Particle Discover

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København N

Postdoc of Machine Learning and Postdoc of Particle Physics
Department of Computer Science
and the Niels Bohr Institute
Faculty of Science
University of Copenhagen


For the GPT4LHC research project, the Department of Computer Science and the Niels Bohr Institute invite applicants for two Post-doctoral fellowships of 24 months duration each.
The GPT4LHC projects aims to explore the usefulness of recent machine learning models based on self-attention such as the generative pre-trained transformers (GPT) for discovering unknown elementary particles from the vast data available from the Large Hadron Collider (LHC). The positions are funded by a Villum Synergy grant.

The start date for both positions is before March 1, 2024.

The project
The Standard Model of particle physics accurately predicted the existence of the Higgs particle and has passed all experimental tests. However, it does not explain the origin of matter in the Universe, the nature of dark matter, and neutrino masses. Thus, it is widely believed that undiscovered elementary particles exist. The LHC is among the most powerful instruments in physics that has produced abundant data (100,000+ TB) ready to new discoveries. The GPT4LHC project explores the hypothesis that modern technologies such as large language models can be applied to the LHC data to discover systematic outliers in the data, that may reveal unknown elementary particles. This requires novel contributions within both machine learning and particle
physics. We will aim to demonstrate the potential by rediscovering the Higgs boson. Success in this project could pave the way for a new era of LHC discoveries.

Successful candidates will be expected to carry out high quality research within a collaborative environment. The postdocs will be closely working with Associate Professor Oleg Ruchayskiy ([email protected]), Professor Erik B Dam ([email protected]), Associate Professor Troels Petersen ([email protected]), and Assistant Professor Raghavendra Selvan ([email protected]).

Who are we looking for?
We are looking for candidates with expertise broadly within the field(s) of machine learning and high energy physics, with particular emphasis on data analysis and related areas of research. Candidates who are about to complete their PhD are also encouraged to apply if a statement of support with the intended completion date is obtained from their PhD supervisor. Applicants from under-represented groups are encouraged to apply.

Our research groups - and what do we offer?
The University of Copenhagen (UCPH) was founded in 1479 and is the oldest and largest university in Denmark. It is often ranked as the best university in Scandinavia and consistently as one of the top places in Europe. The two Post-Doc positions will be within the Faculty of SCIENCE, one at the Department of Computer Science (DIKU, https://di.ku.dk/english), and one at the Niels Bohr Institute (NBI, https://nbi.ku.dk/english/). The Faculty of SCIENCE at UCPH offers a friendly and
thriving international research and working environment with opportunities to build up internationally competitive research groups.

The Machine Learning Section is a part of DIKU and the ELLIS Unit Copenhagen (https://ellis.eu/). The department is heading 2 centers within Artificial Intelligence: the SCIENCE AI Center and the Pioneer Center within Artificial Intelligence. Within computer science, DIKU is ranked number 2 in the European Union according to the Academic Ranking of World Universities (ARWU) 2021.

The Subatomic Section is part of the Niels Bohr Institute (NBI), and participates in the ATLAS, ALICE, and IceCube experiments. The group has long been active in the application of machine learning to particle physics research. The Niels Bohr Institute has for a century pursued physics research in a wide range of fields, and continue to be a Nordic leader in doing so.

Copenhagen is one of the 10 most livable cities in the world with a rich culture within music, theater, and associations. Life for families is made easy by a publicly supported daycare and health care system, dual career opportunities, maternity/parental leave, and six weeks of paid annual vacation. International candidates may find information on living and working in Denmark here. Useful information is also available at The International Staff Mobility office (ISM) at the University
of Copenhagen (link). ISM offers a variety of services to international researchers coming to and working at the University of Copenhagen.

Terms of employment
The position is covered by the Memorandum on Job Structure for Academic Staff. Terms of appointment and payment accord to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State.

Negotiation for salary supplement is possible.

The application, in English, must be submitted electronically by clicking APPLY NOW below.

Please include:

  • Curriculum vitae
  • Diplomas (Master and PhD degree or equivalent)
  • Research plan – description of current and future research plans (3 pages max)
  • Letter of Motivation (1 page max)
  • Complete publication list
  • Separate reprints of 3 particularly relevant papers

The deadline for applications is November 12, 2023, 23:59 GMT +1.

After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee. You can read about the recruitment process at http://employment.ku.dk/faculty/recruitment-process/.

Kilde: Jobnet.dk


Information og data

Denne ledige stilling har jobtypen "Forsker", og befinder sig i kategorien "Sundhed og forskning".

Arbejdsstedet er beliggende i København N.

Jobbet er oprettet på vores service den 10.10.2023, men kan have været deaktiveret og genaktiveret igen.

Dagligt opdateret: Dette job opdateres dagligt ud fra jobudbyderens hjemmeside via vores søgemaskineteknologi og er aktivt lige nu.
  • Forsker
  • København N
  • Søndag den 12. november 2023

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