The position must start before April 30, 2023!

Announcement

The University of Paris Cité calls for applications for a PhD position in Data Intensive Astroparticle Physics to work under the supervision of Prof. Yvonne Becherini through the funding of the “IdEx Chaire of Excellence”. The candidate will work Machine/Deep Learning for Neutrino and Gamma-Ray Astronomy. The research project will benefit both from the Astroparticule et Cosmologie laboratory (APC) and from the Data Intelligence Institute of Paris (diiP) environments.

Subject field of the position: Physics with specialization in Data Intensive Astroparticle Physics

Placement: University of Paris  Cité, Astroparticule et Cosmologie laboratory (APC) and Data Intelligence Institute of Paris (diiP)

Extent: 100%

Duration of appointment: 3 years

Research project title: Machine/Deep Learning for Neutrino Astronomy

Data access: KM3NeT real data and Monte Carlo simulations

Doctoral school: STEP’UP (Earth and Environment Science and Physics of the Universe in Paris)

Context of the research project

Astroparticle physics is a sub-branch of Physics dealing with the understanding of the Universe through the detection of gamma rays, neutrinos, gravitational waves and cosmic rays. In this context, the origin of IceCube TeV-PeV cosmic neutrinos remains still unknown.

In the context of the KM3NeT project, the candidate will develop advanced analysis methods using Machine/Deep Learning both in a supervised and in an unsupervised way. The goal of the project is to reach optimal sensitivity in all-flavour neutrino detection from astrophysical point-like sources, via an enhanced direction and energy reconstruction method and a powerful atmospheric muon background suppression.

In the context of H.E.S.S., the candidate will participate in the analysis of the “Neutrino Follow-Up” observations, and in the decisions of the observations campaigns. The candidate will perform the Fermi analysis of the sources in the FoV of the arrival direction of the High-Energy neutrinos, and finally gather all multi-wavelength observations and model the overall observed emission with available modelling packages. The candidate is expected to participate in several H.E.S.S. publications.

Description of Group/Laboratory/Supervision

This PhD thesis will be supervised by Yvonne Becherini, Professor at the University of Paris Cité, and will take place within the High-Energy Astrophysics (AHE) group of the AstroParticule and Cosmologie Laboratory and the Data Intelligence Institute of Paris (diiP). The APC is an ideal laboratory for carrying out such a research project, as the lab is a member of KM3NeT. The diiP is the ideal research centre for knowledge exchange on data-intensive aspects. The PhD student will become a member of the KM3NeT and HESS Collaborations.

Proposed work

• Machine/Deep Learning methods development for an optimal all-flavour astrophysical neutrino sensitivity

• Active participation in proposals for, and decisions on HESS Follow-Up observation campaigns

• Analysis of HESS data taken after neutrino alerts (“Neutrino Follow-Up” Approach)

• Analysis based on Python programming

• Writing of scientific articles

• Oral presentations at national and international workshops/conferences

Duties

• Attend doctoral school courses for a total of 15 Academic credits, more information may be found at this address: https://ed560.ed.univ-paris-diderot.fr/en/rules-for-training/

• Work on the research subject proposed in this document

• Regularly presentations of intermediate research results to the supervisor 

• Active participation in the KM3NeT and HESS Collaborations

• Work in close collaboration with the other project members in an interdisciplinary research environment as well as with domain experts

• Presentation and publishing of intermediate results in conference proceedings

• Presentation and publishing of more mature results in journal articles

• Preparation of the thesis manuscript

• Participation to the annual “Congrès des Doctorants”

Training and skills required

• Master in Astronomy and Astrophysics or Master in Astroparticle Physics

• Ability to work in a team

• Python programming

• Excellent command of English

Acquired skills

Several skills acquired and developed during this PhD thesis will be valuable and transferable to other fields: data analysis at different wavelengths, numerical simulations, data processing, data analysis, machine learning, writing of articles and of observation proposals, teamwork, oral presentations at national and international workshops and conferences.

Assessment criteria

The selection of candidates is made with regard to the applicant’s ability to successfully complete and benefit from their studies at the graduate level. The assessment takes into account academic skills documented in scientific works, especially focused on the quality of the essays at the undergraduate level, any advanced work and other scientific or scholarly works. The assessment also takes into account breadth and composition of the undergraduate degree.

The successful candidate has excellent analytical and problem-solving skills, is a committed researcher with a drive for excellence. Prior research experience concerning the subject is a significant advantage. Excellent written and oral communication skills in English are essential to publish and present results at international conferences and in international journals. Advanced skills in computing are a key requirement, as all activities are carried out in Linux/Unix environments and using the Python programming language. Interpersonal skills and flexibility are of key importance since the work is done in a research group.

Required documents

Send a cover letter, a CV, links to the Master thesis and previous works, Bachelor and Master education grades, and contact information of two referees to yvonne.becheriniapc.in2p3.fr.
The referees can send the letters directly to yvonne.becheriniapc.in2p3.fr.
After the selection of the candidate, the doctoral school needs to endorse the nomination. The contract can start shortly after the PhD candidate has been nominated, but before April 30, 2023.