INESC TEC is now accepting grant applications to award 1 Research Grant (BI) within the scope of the AgWearCare funded by European Regional Development Fund (ERDF) and by the Operational Programme for Competitiveness and Internationalisation, project (reference NORTE-01-0247-FEDER-072624.
GRANT DESCRIPTION
Type of grant: Research Grant (BI)
General scientific area: COMPUTER SCIENCE
Scientific subarea: Programming Informatics
Grant duration: 6 months, starting on 2023-01-01.
Scientific advisor: João Mendes Moreira
Workplace: INESC TEC, Porto, Portugal
Maintenance stipend: € 1144,64, according to the table of monthly maintenance stipend for FCT grants (http://www.fct.pt/apoios/bolsas/valores), paid via bank transfer. Grant holders may be awarded potential supplements, according to a quarterly evaluation process (Articles 19, 21 and 22 of the Regulations for Grants of INESC TEC and Annex II), up to a maximum limit of 50% of the monthly maintenance stipend. INESC TEC supports costs with registration, enrolment or tuition fees, during the grant duration, under the terms established in the internal document: "Payment of Tuition fees to grant holders". The grant holder will benefit from health insurance, supported by INESC TEC.
OBJECTIVES:
- Creation of a monitoring system for metrics analysis that supports the management of available human resources, assisting decision-making both in terms of well-being and productivity.
- Develop, integrate and test on the ground the application developed in order to prove its ability to solve problems.
BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING:
- Study of art stay on health recognition and well-being with machine learning techniques
- Analysis of use cases and definition of specific goals and requirements (for example, what data should be used)
- Collect data and perform preprocessing (e.g. remove noise, damaged data, ...)
- Describe/study data from infrastructure (e.g. wearable data and sensors)
- Create Machine Learning models for Human Activity Recognition using Pocket Data Mining methods.
- Test and validate the models in laboratory and business environment.
REQUIRED PROFILE:
Admission requirements:
- Master's degree in Computer Science, Electrical, Informatic or similar.
- The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions.
Preference factors:
- Enrollment in a doctoral program in Computer Science, Electrical, Informatic or similar; Experience in participating in research projects; Experience in Machine Learning, Data Mining.
Minimum requirements:
- Final graduation mark higher or equal to 14.
- Fluency in English (spoken and written).
Application period: From 2022-11-23 to 2022-12-07
Submission of applications: the application will be formalised by submitting the form available in the Work With Us section of INESC TEC website.
BINDING LEGISLATION AND REGULATION
The hiring process shall comply with the current legislation regarding the Research Grant Holder Statute, approved by Law n.º 40/2004 of August 18, in its current wording, as well as by the Regulations for Grants of INESC TEC and for FCT Grants Regulation in force. For more information, please check the Regulations for Grants of INESC TEC and relevant annexes at www.inesctec.pt/bolsas.
