PRIDE PhD Fellow (Doctoral Candidate) position (m/f)

Updated: 8 months ago
Deadline:

You will be working as part of DRIVEN Doctoral Training Unit (DTU) funded by the FNR PRIDE scheme. The Computational and Data DRIVEN Science DTU will train a cohort of 19 Doctoral Candidates who will develop data-driven modelling approaches common to a number of applications strategic to the Luxembourgish Research Area and Luxembourg’s Smart Specialisation Strategies . DRIVEN will build a bridge between state-of-the-art data driven modelling approaches and particular application domains, including Computational Physics and Engineering Sciences, Computational Biology and Life Sciences, and Computational Behavioural and Social Sciences.

During the last years online video games have exponentially gained in success, and the development of E-Sport is blooming. Due to the increased ubiquity of video use in the general population, a significant number of studies have investigated the potential adverse outcomes and public health issues associated with excessive involvement. There is evidence that excessive use of video games is associated with mental health problems and interpersonal conflicts. In May 2019, the World Health Organization included (Video) Gaming Disorder as a new mental condition in the eleventh International Classification of Disease (ICD-11). In this context, machine learning offers an original and powerful approach to test the validity of existing conceptualizations (including the one proposed by the WHO) of gaming disorder. This research project constitutes one of the first application of machine learning in the field of psychopathology. Under the working title “Improving the diagnosis of Gaming Disorder: an application of machine learning in Psychopathology”, the doctoral candidate will address how machine-learning techniques can address the question which of the newly proposed diagnostic criteria for gaming disorder constitute reliable and valid indicators of pathological video game use, based both on existing databases accessible to the supervisors and ongoing data-collection led by the supervisors. 

Supervision:

Your lead supervisor will be Prof. Dr. Claus Vögele. Further supervision will be provided by Prof. Dr. Joël Billieux and Prof. Stéphane Bordas.


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