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A holistic recommender system for behaviour change throughout the 4 pillars of health
A holistic recommender system for behaviour change throughout the 4 pillars of health

05/APR/2019
05/APR/2019

 

To live longer, healthier and more active, people at any age has to follow simple and clear

suggestions that cover all the 4 pillars of health: physical activity, nutrition, mindfulness, and

sleeping. Unfortunately, due to the intrinsic (e.g., daily-life habits) and extrinsic (e.g.,

environmental change) factors, people are far to have a healthy life and, thus, there is an

increase of chronic diseases, mental disorders, and premature death. In the literature, several

behavioural change theories have been defined and studied to help people to make a change in

their life.

In this Ph.D. thesis, we will investigate how to design, implement, and validate a holistic recommender system inspired by behavioural change theories and taking into account the 4 pillars of health all together.

The recommender system will be designed with AI and ML capabilities able to personalize the experience to the kind of user considering working and daily-life habits, age, gender, health status, and preferences.

To live longer, healthier and more active, people at any age has to follow simple and clear

suggestions that cover all the 4 pillars of health: physical activity, nutrition, mindfulness, and

sleeping. Unfortunately, due to the intrinsic (e.g., daily-life habits) and extrinsic (e.g.,

environmental change) factors, people are far to have a healthy life and, thus, there is an

increase of chronic diseases, mental disorders, and premature death. In the literature, several

behavioural change theories have been defined and studied to help people to make a change in

their life.

In this Ph.D. thesis, we will investigate how to design, implement, and validate a holistic recommender system inspired by behavioural change theories and taking into account the 4 pillars of health all together.

The recommender system will be designed with AI and ML capabilities able to personalize the experience to the kind of user considering working and daily-life habits, age, gender, health status, and preferences.

Requirements:

The candidates are requested to:

  • Have finished a degree in computer science. A double degree with Mathemathics will be highly valued.
  • Be eager to learn new mathematical models and technologies, specifically in the Big Data/Machine Learning area.
  • Have a good knowledge of Python.
  • Be able to work with development tools such as Git.
  • Be a proficient developer.
  • Have excellent marks.
  • Be a heavy worker and motivated by challenges.

The candidates are requested to:

  • Have finished a degree in computer science. A double degree with Mathemathics will be highly valued.
  • Be eager to learn new mathematical models and technologies, specifically in the Big Data/Machine Learning area.
  • Have a good knowledge of Python.
  • Be able to work with development tools such as Git.
  • Be a proficient developer.
  • Have excellent marks.
  • Be a heavy worker and motivated by challenges.

Salary: 

The Phd is a joint collaboration between Eurecat and IIIA-CSIC under the program \"Beques de doctorat Vicente Lopez\" (see https://eurecat.org/eurecat/beques-doctorat-vicente-lopez-2019/)
The Phd is a joint collaboration between Eurecat and IIIA-CSIC under the program \"Beques de doctorat Vicente Lopez\" (see https://eurecat.org/eurecat/beques-doctorat-vicente-lopez-2019/)

Duration: 

3 years
3 years

Workday: 


Workplace: 


Start date: 

September 2019
September 2019

Closing date: 

16/JUN/2019
16/JUN/2019


Please contact Dr. Josep Lluís Arcos if you are interested in the positions by directly sending an e-mail to arcos@iiia.csic.es.

Please contact Dr. Josep Lluís Arcos if you are interested in the positions by directly sending an e-mail to arcos@iiia.csic.es.


Dr. Josep Lluís Arcos (arcos@iiia.csic.es)

Dr. Josep Lluís Arcos (arcos@iiia.csic.es)

IIIA-CSIC. CAMPUS DE LA UAB, E-08193. BELLATERRA, CATALONIA (SPAIN)
Phone. (+34) 93 580 9570 − Fax.  (+34) 93 580 9661
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