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Interactive virtual reality for individuals with complex PTSD

Project Description

The University of Exeter EPSRC DTP (Engineering and Physical Sciences Research Council Doctoral Training Partnership) is offering up to 4 fully funded doctoral studentships for 2019/20 entry. Students will be given sector-leading training and development with outstanding facilities and resources. Studentships will be awarded to outstanding applicants, the distribution will be overseen by the University’s EPSRC Strategy Group in partnership with the Doctoral College.


Professor Krasimira Tsaneva-Atanasova, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences
Dr Anke Karl, Department of Psychology, College of Life and Environmental Sciences
Dr Phil Self, Devon NHS Partnership Trust

Project description:

Individuals with complex PTSD after a history of early trauma show impairments in emotion regulation and social functioning (1) which are difficult to address in existing psychological therapies. Embodied virtual reality (VR) provides an effective, safe and ethically acceptable therapeutic approach for changing social cognition (2,3) and therefore lend themselves for novel psychological treatments (4). The development of interactive VR scenarios requires advanced mathematical modelling and machine learning (5,6). The aim of this PhD project is to develop a comprehensive emotion regulation and social skill training VR platform and conduct a proof-of-principle study in a healthy sample. In particular, the PhD aims to answer (i) What mathematical/machine learning approach supports the development of various training functions? What are the meaningful features and modelling approaches for virtual reality-based therapeutic scenarios (behaviours) for patients with PTSD and (ii) Do the pilot scenarios effectively change the targeted emotion regulation and social competency mechanism. For answering question (i), the student will use advanced data analysis combining statistical and machine learning techniques along with data-driven modelling of human movement and behaviour. For answering question (ii), the student will run an experimental study in analogue samples/healthy controls to investigate if the scenarios are effective in improving the respective target emotion regulation and social interaction function. The PhD project aims to recruit a computer science/mathematics student with the following required qualifications: Mathematical modelling and machine learning, advanced programming skills (Python, Matlab); interest in experimental psychology, ability to work in a multidisciplinary team and with external/industry partners. The student will be supervised by a multidisciplinary team of mathematicians/computer scientists (Professor Krasimira Tsaneva-Atanasova) a clinical psychologist and neuroscientist (Dr Anke Karl) and two external partners; a creative industry partner (James Richards; and a chartered clinical psychologist with expertise in management of severe mental health conditions who is also Head of Psychology and Practice of Devon Partnership Trust (DPT) (Dr Phil Self). Our industry/creative partner, James Richards from Chromatrope Ltd. contributes to this project in kind by supporting the development of 360 degree ecologically valid environments that form the basis of the training environments.

Funding Notes

For successful eligible applicants the studentship comprises:

- An index-linked stipend for up to 3.5 years full time (currently £14,777 per annum for 2018/19), pro-rata for part-time students.
- Payment of University tuition fees (UK/EU)
- Research Training Support Grant (RTSG) of £5,000 over 3.5 years, or pro-rata for part-time students


1. Cloitre, M. (2015). Eur J Psychotraumatol, 6, 27344. doi:10.3402/ejpt.v6.27344
2. Maister L, Slater M, Sanchez-Vives MV, Tsakiris M. Trends Cogn Sci. 2015 Jan;19(1):6-12.
3. Tajadura-Jiménez A, Banakou D, Bianchi-Berthouze N, Slater M. Sci Rep. 2017 ;7(1):9637.
4. Freeman D, Reeve S, Robinson A, Ehlers A, Clark D, Spanlang B, Slater M. Pychol Med. 2017 47(14):2393-2400.
5. Zhai C, Alderisio F, Slowinski P, Tsaneva-Atanasova K, di Bernardo M. IEEE Trans Cybern. 2017 [Epub ahead of print]
6. Zhai C, Alderisio F Słowiński P, Tsaneva-Atanasova K, di Bernardo M. PLoS One. 2016 Apr 28;11(4):e0154361.

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