The major part of the PhD studies can be dedicated to research in the area of Learning for Model Design Support in Multi-Agent Simulation: Developing Multi-Agent Simulation Model is a challenging task that is often done in a suboptimal try and error procedure. Our idea is that it can be supported by learning and adaptive agents cooperating with a human modeler.
There are various interesting starting points for the development and application of learning agent architectures and mechanisms from Artificial Intelligence such as reinforcement learning, classification learning or evolutionary strategies. Simulated agents shall use such techniques for discovering non-valid situations, selecting interaction partners, revising decision rules, etc.
The human modeler evaluates the suggestions of the learning agents or supports them by pointing to particular problematic or interesting situations. Such an interplay between learning agents and human modeler may result in an efficient model development process and finally to a valid Multi-Agent Simulation model. Ideas can be tested in interdiscilinary simulation projects for example in the area of pedestrian simulation.
In addition to interest in the topic, a strong theoretical background and solid programming skills, applicants should have the equivalent of a Masters degree in an appropriate field (for example: Intelligent Systems and Robotics, Computer Science, Physics or Applied Mathematics). It is not necessary to be familiar with the Swedish language but proficiency in written/spoken English is mandatory.
To apply for the position, please send a motivation letter along with an updated CV (including at least two academic references) by e-mail to Dr. Franziska Kluegl: franziska.klugl[ at ]oru.se
Applications can be sent immediately and will be considered until the position is fixed.
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Disclaimer: Every effort has been made to ensure the above information is current and correct. However, applicants should contact the appropriate administering body before making an application, as details do change frequently.