Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- INESC ID
- INESC TEC
- Instituto Pedro Nunes
- FCiências.ID
- Politécnico de Leiria
- Universidade de Coimbra
- FEUP
- Institute of Systems and Robotics, Faculty of Sciences and Technology of the University of Coimbra
- Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial
- University of Minho
- 1 more »
- « less
-
Field
-
) project aims to develop a system based on artificial intelligence (AI) and computer vision for the automatic detection of REM sleep behavior disorder (RBD)-specific behaviors in polysomnography (vPSG
-
the following terms: . SCIENTIFIC AREA: Artificial Intelligence. . RECIPIENTS: Holders of an undergraduate degree in Computer Engineering or related areas who are enrolled in a master course in Computer
-
artificial intelligence-based algorithms to optimise operation and predict anomalies in water distribution networks. The algorithms developed should identify patterns and anomalies that indicate the presence
-
.; - Develop skills in artificial intelligence and machine learning techniques for analyzing operational data and detecting anomalies, using foundational model approaches (e.g., GridFM project, LF Energy
-
- Work Plan / Goals to be achieved: The post holder will join the DETECT project, which aims to develop an artificial intelligence-based tool to identify individuals at clinical high risk for psychosis
-
Artificial Intelligence models for the accurate simulation of motor racing and for the generation of suggestions for effective racing strategies. The research fellow will explore different modelling approaches
-
. Procedure Reference: IT137-25 -388 I - Legal admission requirements I.I - Grant Recipients: Bachelor’s degree in Informatics Engineering, enrolled in a Master’s program in Artificial Intelligence, Data
-
on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: - knowledge of wireless networks; - knowledge of Artificial Intelligence models.; Minimum
-
on the development and implementation of advanced systems in cybersecurity, artificial intelligence, and data science in public administration, as well as a scientific capacity-building programme”, within the funding
-
on the development and implementation of advanced systems in cybersecurity, artificial intelligence, and data science in public administration, as well as a scientific capacity-building programme”, within the funding