We offer a unique opportunity for a post doc of 3-5 years in a highly collaborative and international environment at the intersection of machine learning and human-computer interaction applied to wellbeing and eHealth.
Stimulating Research Environment.
The Helsinki Area is an attractive environment for HCI and AI. The newly established Finnish Centre for Artificial Intelligence provides a collaboration platform to learn and network.A local community of HCI faculty meets monthly and organises a variety of events (industry meeting, seminars, tutorial, etc.) The position benefits from a European project funding and network with excellent possibilities for research visits.
The aim of the research is to develop machine learning techniques to infer mental and physical wellbeing of users. Inferring the user wellbeing state will be used to personalise recommendations the are delivered through a chat bot or used to personalise a immersive VR environment . The user models use data comping from wearable physiological sensors (EDA, HRV, EEG) , content from chatbot conversations, and interaction log data. The research continues highly successful research on user modelling from screen monitoring extracting textual content from all that happens on the screen (Vuong et al 2017), research on detecting relevance or affect from physiology as a response to news or other documents such as comic strips (Eugster et al 2016, Barral et al 2018, Barral et al 2016), and research on studying affective interaction in VR (Ravaja et al 2017, Kosunen et al 2016) .
You have a PHD in machine learning and interest in applying advanced ML techniques in human computer interaction. Experience in health, wellbeing and affective computing is appreciated as also experiences of game development, multimodal interaction and ubiquitous computing.
The salary will depend on your qualifications and follows the Finnish salary system for universities.
The position will support developing your profile towards a world class expert in machine learning applied to well being for industry or for academic carrier. You will be supported in joining academic conferences and committees, teaching one course or seminar.
Applications by 27th of February
Send your application containing your CV, references, publication list, and introduction letter explaining how your background, skills and interest match the position.
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Vuong, T., Jacucci, G., & Ruotsalo, T. (2017). Watching inside the Screen: Digital Activity Monitoring for Task Recognition and Proactive Information Retrieval. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(3), 109.
Eugster, M. J., Ruotsalo, T., Spapé, M. M., Barral, O., Ravaja, N., Jacucci, G., & Kaski, S. (2016). Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals. Scientific reports, 6, 38580.
Barral, O., Kosunen, I., & Jacucci, G. (2018). No Need to Laugh Out Loud: Predicting Humor Appraisal of Comic Strips Based on Physiological Signals in a Realistic Environment. ACM Transactions on Computer-Human Interaction (TOCHI), 24(6), 40.
Barral, O., Kosunen, I., Ruotsalo, T., Spapé, M. M., Eugster, M. J., Ravaja, N., ... & Jacucci, G. (2016). Extracting relevance and affect information from physiological text annotation. User Modeling and User-Adapted Interaction, 26(5), 493-520.
Ruotsalo, T., Jacucci, G., Myllymäki, P., & Kaski, S. (2015). Interactive intent modeling: Information discovery beyond search. Communications of the ACM, 58(1), 86-92.
Kosunen, I., Salminen, M., Järvelä, S., Ruonala, A., Ravaja, N., & Jacucci, G. (2016, March). RelaWorld: neuroadaptive and immersive virtual reality meditation system. In Proceedings of the 21st International Conference on Intelligent User Interfaces (pp. 208-217). ACM.
Ravaja, N., Harjunen, V., Ahmed, I., Jacucci, G., & Spapé, M. M. (2017). Feeling touched: Emotional modulation of somatosensory potentials to interpersonal touch. Scientific reports, 7, 40504.
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