Eurograd message

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).*
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<br>
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<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>
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<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>
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<br>*Position 1: Designing care robots*
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<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>
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<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>
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<br>Interested? Please contact us with any questions (Ericka Johnson <
<br>ericka.johnson@liu.se> and Katherine Harrison )
<br>
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<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
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