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Background:

Volcán Popocatépetl, whose name is the Aztec word for smoking mountain, towers to 5426 m 70 km SE of Mexico City to form North America’s 2nd-highest volcano.  The glacier-clad stratovolcano contains a steep-walled, 250-450 m deep crater.  The generally symmetrical volcano is modified by the sharp-peaked Ventorrillo on the NW, a remnant of an earlier volcano. 
At least three previous major cones were destroyed by gravitational failure during the Pleistocene, producing massive debris-avalanche deposits covering broad areas south of the volcano.  The modern volcano was constructed to the south of the late-Pleistocene to Holocene El Fraile cone.  Three major plinian eruptions, the most recent of which took place about 800 AD, have occurred from Popocatépetl since the mid Holocene, accompanied by pyroclastic flows and voluminous lahars that swept basins below the volcano.  Frequent historical eruptions, first recorded in Aztec codices, have occurred since precolumbian

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Research Opportunities

Bioengineering, Computer Engineering and Computing, Electrical and Computing, Artificial Intelligence and Data Science and related areas

Work description

The fellow to be hired will contribute to the development of artificial intelligence-based solutions for automated analysis of polysomnography (vPSG) videos in the context of the diagnosis of sleep disorders, in particular REM sleep behavior disorder (RBD).

The activities to be performed aim to:

Annotation and preparation of data for training and validation of machine learning models, ensuring the quality and coherence of the data sets used. Development and implementation of computer vision algorithms for detecting and analyzing specific RBD behaviors in vPSG videos. Integration and testing of solutions in a simulated clinical environment, ensuring the applicability of the models developed in medical practice. Participation in the analysis of results and in the benchmarking of the system against the manual evaluation of experts,

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Job ID: 256548

INESC TEC | Research Initiation Grant (AE2025-0182)
INESC TEC

Research Opportunities

Computer Science

Work description

Study methodologies for automatic detection of anomalies or regions of interest; Collaborate in the preparation of suitable datasets; Implement modules for processing the visual information; Writing the grant activity report.

Academic Qualifications

Degree in Electrical and Computer Engineering, Informatics, Computer Science or similar

Minimum profile required

Programming experience (C/C++/Python); experience in computer vision; Experience in deploying machine learning models;

Preference factors

Enrolled in a master course; Experience in the usage of computer vision and machine learning models for object detection in industrial environments.

Maintenance stipend: € 651.12, according to the table of monthly maintenance stipend for FCT grants , paid via bank transfer. Grant holders may be

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