[ecoop-info] 1-2 Ph.D positions on Combining Formal Methods and Machine Learning (U. of Oslo), 9. June 2017

Martin Steffen msteffen at ifi.uio.no
Thu May 4 16:11:33 CEST 2017


		Up to 2  PH.D POSITIONS in Combining Formal Methods and
		Machine Learning

			deadline: 9. June 2017

                Dept. of Computer Science, University of Oslo


PhD Research Fellow , 1-2 positions

* Context

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.

* Job description

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.

* Requirements

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.

Additional formal requirements (excepted documents, procedural questions,
etc.) as well as information concerning the university as workplace is
available via the link given above.

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