PhD student in astronomical classification with 4MOST
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The research conducted at the Department of Physics and Astronomy encompasses a wide range of physics topics, distributed over ten divisions. The department is located in the Ångström laboratory and employs nearly 400 people, 125 of whom are doctoral students. It offers a broad physics curriculum to undergraduate and graduate students, participation in nationally and internationally leading projects for researchers, and opportunities for partnership with industry and various outreach activities. Read more at www.physics.uu.se.
The Division for Astronomy and Space Physics conducts research on galaxies and cosmology, stellar physics, planetary systems, atomic astrophysics and instrumentation. The Division is participating in a range of international scientific collaborations around telescopes and satellites. We are now hiring a PhD student in astronomy, focused on astronomical classification with the telescope 4MOST (4-metre Multi-Object Spectroscopic Telescope).
This PhD position is part of the eSSENCE - SciLifeLab graduate school in data-intensive science. The school addresses the challenge of data-intensive science both from the foundational methodological perspective and from the perspective of data-driven science applications. It is an arena where experts in computational science, data science and data engineering (systems and methodology) work closely together with researchers in (data-driven) sciences, industry and society to accelerate data-intensive scientific discovery.
eSSENCE is a strategic collaborative research programme in e-science between three Swedish universities with a strong tradition of excellent e-science research: Uppsala University, Lund University and Umeå University.
SciLifeLab is a leading institution and national research infrastructure with a mandate to enable cutting-edge life sciences research in Sweden, foster international collaborations, and attract and retain knowledge and talent.
The PhD student is expected to work on the development and implementation of astrostatistical machine learning methods for automatic classification and anomaly detection in analyses of complex and large astronomical data sets. The work will be performed in the context of the scientific collaboration around the telescope 4MOST (4-metre Multi-Object Spectroscopic Telescope, http://www.4most.eu).
4MOST will be Europe's new workhorse for astronomical spectroscopy. Starting in 2024, 4MOST will collect optical spectra of 40 million objects, both Galactic and extra-galactic ones. Uppsala University has been a science partner in the project from the start. To maximize the science output and the potential for discovery, we require novel and efficient methods for object classification and anomaly detection, that do not rely on pre-existing theoretical or empirical models. The full 4MOST data set will contain around three trillion data points. This necessitates a probabilistic classification method which can be repeatedly and efficiently re-trained.
The goal of the PhD project is to develop an efficient automatic and dynamically trainable classification and anomaly detection pipeline for spectroscopic + photometric astronomical surveys, which can compute probabilities for each object (astronomical source) in very large data sets to belong to different categories of objects (e.g. stars, galaxies, active galactic nuclei). To achieve this, supervised and unsupervised Bayesian machine learning methods will be adapted and employed, as well as methods for handling vast and high-velocity data sets. Initially, the work will be performed using data from earlier astronomical surveys and simulations, and is expected to transition to using observational data from 4MOST in 2024/25.
The PhD student will belong to the eSSENCE – SciLifeLab graduate school in data-intensive science, and is expected to participate and collaborate within the graduate school.
The candidate must have a Master’s degree in astronomy, physics, engineering physics, computer science or a related discipline. The candidate should have experience from programming in some modern programming language, and good communication skills as well as good oral and written proficiency in English. The appointed person must also be able to both work independently and collaborate in an international team with partners in other countries.
Experience in astronomy / astrophysics, spectroscopy and machine learning methods as well as study results will be considered very important merits. Experience in scientific computing will be considered an important merit. Experience of analysis of large or high-velocity data sets will also be considered merits. Interviews and aptitude tests may be conducted as part of the selection procedure.
Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines.
About the employment
The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date as agreed. Placement: Uppsala.
For further information about the position, please contact: Martin Sahlén, +46-18-471 59 70, firstname.lastname@example.org; Andreas Korn, email@example.com.
Please submit your application by 23 May 2022, UFV-PA 2022/1263.
About the application
The application should include a curriculum vitae (CV), a short description of research interest, qualification and earlier research experience, copies of relevant grade certificates, master thesis, any scientific publications, and a cover letter. Contact information for two independent referees (e-mail and phone number) as well as personal circumstances that may be relevant for the assessment (e.g. parental leave) should be included in the applicant’s CV. The application should be written in English.
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Placement: Department of Physics and Astronomy
Type of employment: Full time , Temporary position longer than 6 months
Pay: Fixed salary
Number of positions: 1
Working hours: 100 %
County: Uppsala län
Seko Universitetsklubben firstname.lastname@example.org
Number of reference: UFV-PA 2022/1263
Last application date: 2022-05-23
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