Fully funded industrial PhD fellowship in the field of computer science

Applications for a fully funded industrial PhD fellowship at fluxLoop and Kristiania University College in the field of computer science.

Proposed research:

The overall goal of the research project is to enable automated ticketing (Be-In, Be-Out) and new business models within the area of public transport, and to improve trip flow modelling and behavior analysis for the mobility industry. The overall goal is a friction free, safe and fair travel experience that stimulate for increased use of environmentally friendly transport.

All of this is possible through the use of unique technology. With sensors in people’s mobile phones and other data sources, one can determine the mobile phone’s position with very high accuracy without compromising user privacy. The mobility players will have the foundation for automatic ticketing and get more insights to further optimizing the service through the use of this platform.

For travelers, this will be a “ticket free” experience that also provide a fairer pricing, with added positive benefits to the environment. The potential for this new technology is global.

Research Project:

We are looking for a candidate to be part of a great team of researchers and developers located in the central part of Oslo. The candidate will spend time both at the fluxLoop office and at the Mobile Technology Lab at Kristiania University College in Oslo.

Kristiania University College is offering a fully funded Ph.D. fellowship in computer science together with fluxLoop. The candidate will be included in a team of researchers with ambitious plans to further develop research activities related to the described research project.

The main activities will be centered around:

-      Defining and researching data and algorithms to precisely indicate if a person is in a mode of transport.

-      Optimize algorithms to be able to run on a mobile device, in addition to the cloud and all the while not compromising user experience, user privacy and performance issues.

-      Participate in prototype development and pilot testing together with public transportation companies in Norway.

-      Find a way to scale the pilot to a bigger scale, to be able to verify that the solution can be rolled into production environments with millions of users.

-      Map and test the optimal ways to understand people’s travel behavior and trip flow.

The PhD fellowship will be over 3 to 4 years, starting at the earliest convenience, with supervisor from the Mobile Technology Lab headed by Prof. Grønli, ending with a completed doctoral degree. The fellowship is fully funded through a research grant from the Research Council of Norway.

For this PhD fellowship, the required core qualifications are:

-      Master’s degree in computer science, informatics, data science or other related disciplines.

-      Strong analytical skills.

-      Proficiency in at least one scientific computing language (Matlab, Python or R).

-      Good proficiency in oral and written English.

-      Excellent team and communication skills.

-      Good ability of working independently.

In addition, the following skills and competences are desirable

-      Knowledge around mobile SDK development and battery optimizations

-      Proficiency in at least one machine learning or data analytics framework or platform (e.g. scikit-learn, Core ML, TensorFlow, Weka)

Applications:

-      Motivation letter for the position

-      Approved Diplomas and Transcripts (in Scandinavian or English language)

-      CV

-      At least two references

-      Master thesis

-      A proposal for a research project of five pages that includes a brief overview of the understanding of the described research issue and reflections on methods and theoretical perspectives. 

Applications can be sent through FINN.

Contact:

Tor-Morten Grønli

Professor Computer Science

Director Mobile Technology Lab

+47 48156476

Karoline Hauge

Project Lead

+47 90 80 30 44

Ulrik Prøitz

CEO fluxLoop

+47 41 30 33 75