Open PhD positions in computational interaction and interactive AI: Deadline Jan 31
Helsinki ICT network: Positions for Exceptional Doctoral Students (deadline January 31, 2019)
The Helsinki Doctoral Education Network in Information and Communications Technology (HICT) is a joint initiative by Aalto University and the University of Helsinki, the two leading universities within this area in Finland. The network involves at present over 60 professors and over 200 doctoral students, and the participating units graduate altogether more than 40 new doctors each year.
The quality of research and education in both HICT universities is world-class, and the education is practically free as there are no tuition fees for doctoral students in the Finnish university system. In terms of the living environment, Helsinki has been ranked as one of the world’s top-10 most livable cities (Economist, 2017), and Finland is among the best countries in the world with respect to many quality of life indicators, including being the overall #1 country in human wellbeing. Helsinki is in the second place in the world’s startup city comparison (Valuer, 2018) and is also the Mobile Data Capital of the World (IEEE Spectrum, 2018).
We welcome applicants with diverse backgrounds, and qualified female candidates are explicitly encouraged to apply. For more information, please see the list of positions (Spring call 2019 projects) and detailed information on the application process below.
The online application form closes January 31, 2019 at midnight Finnish time.
Project 6: Open doctoral student position in Prof. Antti Oulasvirta’s group – computational methods in HCI
Supervisor: Prof. Antti Oulasvirta (Aalto University, Department of Communications and Networking)
This ERC funded group is looking for PhD students interested in applications of computational methods in HCI. Background and interest in data science, machine learning, modeling, neurosciences, or cognitive science is required. PhD topic will be negotiable. On-going topics in 2018 include, but are not limited to: 1) Interaction techniques for collaborating with an artificial intelligent agent in complex scientific tasks; 2) modeling of input using control theory, neuromechanics; 3) reinforcement learning models of human-computer interaction; 4) computational models of emotion.