Machine-learning Models in the Context of Physiological State Transitions

OPPORTUNITY DETAILS

Total reward

0 $

State University

KU Leuven

Area

Western Europe

Host Country

Belgium

Deadline

01 Mar 2021

Study level

Doctoral Degree

Opportunity type

PhD

Specialities

Engineering , Information technology and computer science , Medicine , Mathematics

Opportunity funding

Not funding

Eligible Countries

This opportunity is destined for all countries

Eligible Region

All Regions

(ref. BAP-2021-45)

This project is the product of a collaboration between IMEC, world leader in nanotechnology and wearables, and the Mind-Body Research group (MBR) and the Centre for Contextual Psychiatry (CCP), two research centers within the Research Group Psychiatry, KU Leuven, Belgium. The MBR group has many years of expertise in stress research and the CCP is internationally recognized for its expertise in measuring psychological variables in daily contexts. Both groups are also closely linked to the University Psychiatric Center (UPC KU Leuven), which facilitates patient access for clinical trials. There is also possibly to collaborate with researchers from the Computational Wellbeing Group at Rice University, Houston, with the option to go abroad on a research visit at that group during the PhD.

Website unit

Advances in wearable technology allow for the continuous assessment of physiological data to get a detailed picture of daily-life dynamics associated with the development of mental illness. Specific physiological state transitions may hold prognostic value in the course of development, treatment, and relapse of mental disorders. Computational modelling of the physiological data is a promising approach in predicting this course. Physiological state transitions can be observed in different time-windows. On a momentary level, minute-to-minute changes in physiology, such as in the case of acute stress and recovery from acute stress, may predict specific illness-related behavior and symptoms. On the longer term, more structural alterations in physiology such as chronic stress or patterns in circadian rhythm may signal important phases in illness progression, treatment effects, or relapse.

The successful applicant will develop new computational models to detect daily life markers of state transitions related to mental health. They will work on existing datasets that have been collected within IMEC and the Psychiatry Research Group of the KU Leuven in healthy volunteers and individuals with psychiatric complaints.

1. Master's degree in electrical or computer engineering, applied mathematics, or related disciplines.

2. Experience in one or more of the following areas is desirable:

- Interest in psychopathology

- Curiosity and ambition to learn new skills and techniques

- Strong communication and social skills.

3. Proficient in English, preferably also in Dutch

The successful candidate will receive a full-time, 4-year PhD position at KU Leuven and will be embedded in an innovative, high-tech working environment in the MBR and CCP research groups, in close collaboration with IMEC. The PhD candidate will collaborate closely with data scientists at IMEC and there is also a possibility to collaborate with researchers from the Computational Wellbeing Group at Rice University, Houston

For more information please contact Dr. Thomas Vaessen, mail: thomas.vaessen@kuleuven.be or Mrs. Martine van Nierop, mail: Martine.vannierop@kuleuven.be

You can apply for this job no later than March 01, 2021 via the online application tool

KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.