PhD Research Fellow in Machine Learning
Universitetet i Oslo
Gaustadalleen 23B, 0373 Oslo
Om jobben
Stillingstittel
PhD Research Fellow in Machine Learning
Type ansettelse
Engasjement, heltid 100%
Arbeidsspråk
Engelsk
Antall stillinger
1
About the position
PhD Research Fellow Position in Machine Learning is available in the Department of Informatics at the University of Oslo (UiO) in collaboration with the SFI Visual Intelligence and Institute of Marine Research.
No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo. Preferred starting date no later than September 1, 2024.
The fellowship period is three (3) years.
A fourth year may be considered with a workload of 25 % that may consist of teaching, supervision duties, and/or research assistance. This is dependent upon the qualification of the applicant and the current needs of the department.
Knowledge development in a changing world - Science and technology towards 2030.
The Faculty of Mathematics and Natural Sciences
Job description
This Ph.D. position is focused on machine learning in realistic settings referring to statistical and system characteristics such as reliability and robustness to limited data and distribution shifts. For the application side, the candidate will collaborate with the Institute of Marine Research on valuable image data of the marine environment.
This position is placed in the Ole-Johan Dahl Building, close to the Forskningsparken metro and tramway station in Oslo. The candidate has the opportunity to collaborate with our broad network of collaborators in the SFI Visual Intelligence, Norwegian Centre for Knowledge-driven Machine Learning (Integreat), and our top-notch international collaborators such as EPFL, Vector Institute, and the University of Toronto.
You will be part of Visual Intelligence and the Digital Signal Processing and Image Analysis (DSB) group. We expect that you will engage in collaborative research with other members of the centre and the research group. You will collaborate with user partners within Visual Intelligence, to contribute to the centre’s seminars, to collaborate across innovations areas within the centre, and to seek collaboration between the research partners within the centre. You will be part of a network of young researchers in deep learning in the Visual Intelligence Graduate School https://www.visualintelligence.no/about/vigs
Qualification requirements
The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials.
Required qualifications:- Applicants must hold a Master’s degree or equivalent in computer science, mathematics, data science, or related fields. Foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system.
- Proficiency in scientific programming (Python).
- Proficiency in deep learning frameworks (Tensorflow or Pytorch).
- Strong background in machine learning and linear algebra.
- Fluent oral and written communication skills in English
- Experience with broader topics in computer vision, theoretical computer science, probability and statistics, game theory, and optimization.
- Experience in writing papers in top conferences/journals.
- Efficient coding skills in C or C++.
- Experience in high performance computing and tooling in Linux.
- Manuscript writing skills in LaTeX.
Candidates without a master’s degree have until 30 June 2024 to complete the final exam.
Grade requirements:
The norm is as follows:
- The average grade point for courses included in the Bachelor’s degree must be C or better in the Norwegian educational system
- The average grade point for courses included in the Master’s degree must be B or better in the Norwegian educational system
- The Master’s thesis must have the grade B or better in the Norwegian educa-tional system
- English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements:
The purpose of the fellowship is research training leading to the successful completion of a PhD degree.
The fellowship requires admission to the PhD programme at the Faculty of Mathematics and Natural Sciences. The application to the PhD programme must be submitted to the department no later than two months after taking up the position.
For more information see:http://www.uio.no/english/research/phd/
http://www.mn.uio.no/english/research/phd/
Personal skills
- Passion for machine learning, theory, scientific programming, and problem solving
- Ability to carry out and complete major tasks
- Ability for giving and receiving constructive scientific criticism
- Willingness to be part of a team and to share knowledge and skills
We offer
- Salary NOK 532 200 – 575 400 per year depending on qualifications and seniority as PhD Research Fellow (position code 1017)
- Attractive welfare benefits and a generous pension agreement
- Vibrant international academic environment Career development programmes
- Oslo’s family-friendly surroundings with their rich opportunities for culture and outdoor activities
How to apply
The application must include:- Cover letter - statement of motivation and research interests
- CV (summarizing education, positions and academic work - scientific publications)
- Copies of the original Bachelor and Master’s degree diploma, transcripts of records and
- Documentation of English proficiency
- List of publications and academic work that the applicant wishes to be considered by the evaluation committee
- Names and contact details of 2-3 references (name, relation to candidate, e-mail and telephone number)
The application with attachments must be delivered in our electronic recruiting system (please follow the link “Apply for this job”). Foreign applicants are advised to attach an explanation of their University's grading system. Please note that all documents should be in English or a Scandinavian language.
Interviews with the best qualified candidates will be arranged.
Formal regulations
Please see the guidelines and regulations for appointments to Research Fellowships at the University of Oslo.
According to the Norwegian Freedom and Information Act (Offentleglova) information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure.
UiO has an agreement for all employees, aiming to secure rights to research results a.o.
Inclusion and diversity are a strength. The University of Oslo has a personnel policy objective of achieving a balanced gender composition. Furthermore, we want employees with diverse professional expertise, life experience and perspectives.
If there are qualified applicants with disabilities, employment gaps or immigrant background, we will invite at least one applicant from each of these categories to an interview.
Contact information
For further information please contact Associate Professor Ali Ramezani-Kebrya, e-mail: ali@uio.no or Professor Anne Solberg, e-mail: anne@ifi.uio.no.
For questions regarding Jobbnorge, please contact HR Adviser Therese Ringvold, e-mail: therese.ringvold@mn.uio.no
Om bedriften
The University of Oslo is Norway’s oldest and highest rated institution of research and education with 28 000 students and 7000 employees. Its broad range of academic disciplines and internationally esteemed research communities make UiO an important contributor to society.
The Department of Informatics (IFI) is one of nine departments belonging to the Faculty of Mathematics and Natural Sciences. IFI is Norway’s largest university department for general education and research in Computer Science and related topics.
The Department has more than 1800 students on bachelor level, 600 master students, and over 240 PhDs and postdocs. The overall staff of the Department is close to 370 employees, about 280 of these in full time positions. The full time tenured academic staff is 75, mostly Full/Associate Professors.
Sektor
Offentlig
Nettsted