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 at the University of Oslo. IFI is Norway’s largest university department for general education and research in Computer Science and related topics. The Department has near 950 students on bachelor level, near 450 master students, and over 180 PhD students. The overall staff of the Department is close to 250 employees, about 200 of these are full time positions. The full time scientific staff is 60, mostly Full/Associate Professors.
PhD Research Fellow Combining Formal Methods and Machine Learning, 1-2 positions
A position as PhD Research Fellow in Formal Modeling and Analysis is available at the Department of Informatics, University of Oslo. The fellowship will be for a period of 3 years with no compulsory work and with possibility to extend to 4 years with 25 % compulsory work (teaching responsibilities at the Department or innovation activities in the SIRIUS Center). Starting date is as soon as possible.
The position is funded by SIRIUS (Center for Scalable Data Access in the Oil and Gas Domain), a new Center for Research-Driven Innovation (SFI) at the University of Oslo. It constitutes a long-term research initiative, funded by the Norwegian Research Council involving both academic research teams (UiO, NTNU and Oxford University) as well as industrial partners including operators (Statoil), service companies (Schlumberger and DNV GL) and IT companies (e.g., Computas, Evry, IBM). The center has as its main goal to develop novel technologies to improve our ability to extract and exploit information from large data stores. The position is hosted at the IFI SIRIUS center, the Execution Modeling and Analysis group, where we research systematic model exploration techniques to predict the behavior of software/system executions based on the analysis of models.
The main focus of this PhD project will be to develop and study new model-based engineering techniques for context-dependent adaptive systems, exploiting the interplay between systematic model exploration and machine learning techniques. In context-dependent adaptive systems, system behavior depends on some contextual information (e.g., data coming from sensors) and the systems must adapt based on their interactions with the environment. We are interested in investigating techniques that combine formal executable modeling with reinforcement learning algorithms to calibrate models and simulate system behavior where its performance improves over time. Demands on analysis techniques to understand context-dependent adaptive systems are increasing in many industrial areas, such as manufacturing, healthcare, oil&gass, and automotive industries. Through SIRIUS, the PhD student will have the opportunity to collaborate with industry and to apply the developed techniques on real industrial cases.
Applicants should submit a statement of research interests or a project outline for the PhD project, but it is expected that the successful candidate will ultimately define their project jointly with their supervisors during the first two months of the fellowship.The application letter should discuss at least one research topic of interest to the candidate, including a brief reflection about the scientific issues involved and the possible choice of theory and method(s). This statement of research purpose should not exceed one page.
The Faculty of Mathematics and Natural Sciences has a strategic ambition of being a leading research faculty. 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.
Applicants must hold a Master's degree or equivalent in a relevant field such as computing/informatics/software engineering/machine learning. A solid background in computing science or software engineering is required.
Good knowledge on algorithms, formal methods, machine learning, and software development skills and experiences will be considered an advantage when candidates are ranked.
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:
Salary: Pay grade: NOK 432 700 – 489 300 per year, depending on qualifications and seniority.
The application must include:
- Application letter, including a summary of research interests or project outline
- CV (summarizing education, positions and academic work - scientific publications)
- Copies of educational certificates, transcript of records and letters of recommendation
- List of publications and academic work that the applicant wishes to be considered by the evaluation committee
- Research proposal describing aims for the PhD research
- Names and contact details of 2-3 references (name, relation to candidate, e-mail and telephone number)
Foreign applicants are advised to attach an explanation of their University’s grading system. Please remember that all documents should be in English or a Scandinavian language.
In accordance with the University of Oslo’s equal opportunities policy, we invite applications from all interested individuals regardless of gender or ethnicity.
UiO has an agreement for all employees, aiming to secure rights to research results a.o.
For further information please contact:
Associate Professor Ingrid Chieh Yu, email@example.com
For questions about the recruitment system, please contact HR-Officer Helene Jansen, firstname.lastname@example.org
- Søknadsfrist9. juni 2017
- ArbeidsgiverUniversity of Oslo
- Intern-ID2017/4872 - 1014
- Søknader for denne stillingen registreres i et elektronisk skjema på jobbnorge.no
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