Message posted on 13/12/2019
3 PhD positions, Caring robots, Tema Genus, Linköping, Sweden
*We are currently accepting applications to 3 fully funded, 4 year PhD <br>positions associated with the research project, ‘The ethics and social <br>consequences of AI and caring robots. Learning trust, empathy and <br>accountability’. * <br> <br>*(deadline 30 January 2020, start date August 2020).* <br> <br> <br> <br>*The project is led by Ericka Johnson and Katherine Harrison at Tema Genus, <br>Linköping University, Sweden. More information can be <br>found: https://liu.se/en/research/caring-robots <br>* <br> <br> <br> <br>*The PhD positions are fully funded (i.e. provide full-employment within <br>the Swedish system, including paid holidays and other standard social <br>benefits, etc.) and can be extended up to a fifth year by teaching <br>opportunities if applicable. * <br> <br> <br> <br>*Position 1: Designing care robots* <br> <br>What bodies are assumed in the design of companion robots, and how does the <br>design of the robot affect its interactions with humans? This project <br>focuses on how care and affect are materialised in the body of the <br>companion robot, with particular critical attention to intersections of <br>gender, ethnicity and ability. An additional area of inquiry could examine <br>how the material design features of the robot's body are mediated through <br>affective programming software to produce a more intimate encounter. <br> <br> <br> <br>*Position 2: Learning data for companion robots. * <br> <br>How can robots learn to care when collecting data on relevant humans may be <br>limited for ethical reasons? Or if real data contain bias, on which data <br>should you train your data? Generative machine learning techniques (such as <br>generative adversarial networks (GANs)) offer a solution to problems with <br>“real” data such as scarce availability, labour intensity of data <br>labelling, data biases, or privacy intrusiveness. This project comprises a <br>critical inquiry into the production/collection of data sets used to help <br>companion robots learn, and particularly the possibility of using GANs to <br>assist with this. <br> <br> <br> <br>*Position 3: The affective space between human and companion robots* <br> <br>Current advances in robotics often focuses on refining robots to learn <br>about and respond better to humans. However, interacting well with a robot <br>also requires significant learning on the part of the human participant. <br>This project focuses on the affective space between human and robot, and <br>the work that both participants must learn to do to create an emotional <br>relation characterised by care and trust. <br> <br> <br> <br>Interested? Please contact us with any questions (Ericka Johnson < <br>ericka.johnson@liu.se> and Katherine Harrison ) <br> <br> <br> <br>Applications are made through the Linköping University web interface: <br>https://liu.se/en/work-at-liu/vacancies?rmpage=job&rmjob=12652&rmlang=UK <br>_______________________________________________ <br>EASST's Eurograd mailing list <br>Eurograd (at) lists.easst.net <br>Unsubscribe or edit subscription options: http://lists.easst.net/listinfo.cgi/eurograd-easst.net <br> <br>Meet us via https://twitter.com/STSeasst <br> <br>Report abuses of this list to Eurograd-owner@lists.easst.netview formatted text
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