Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
appropriate methodologies and proceed with the development of optical sensors for monitoring dissolved CO2; Design, implement, and test optoelectronic systems for characterizing the developed sensor; Develop
-
contribute to the development of ALDFG (“Abandoned, Lost, Derelict Fishing Gear”) detection solutions based on information from acoustic sensors on board autonomous marine vehicles Carry out a project course
-
) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Study appropriate methodologies and develop optical sensors for monitoring various water quality parameters.; Design
-
. Implementation of signal detection algorithms and triangulation ; 4. Planning and participating in field tests to evaluate system performance; 5. Reporting and disseminating the work developed (ideally with a
-
algorithms; - Automation of the model customization process by conducting laboratory tests.; - Improvement of the data workflow for real-time processing and sharing.; - Data collection in experimental and real
-
algorithms to combine information on cardiovascular activity obtained from heart sound signals, electrocardiogram, and photoplethysmography. Investigate the inclusion of prior knowledge about the application
-
algorithms for free-flying robots in microgravity.; • Implement and validate real-time multi-target tracking techniques.; • Validate the algorithms in simulated scenarios, evaluating robustness and accuracy
-
suitable voltage and frequency control strategies, based on state-of-the-art research, and development of dispatch algorithms for the isolated microgrid, considering the coordinated control of generation
-
OF THE WORK PROGRAMME AND TRAINING: 1) Development of workflows and algorithms to complement datasets of connected data spaces, to improve analysis results (forecasting, analysis of financial tools, predictive
-
) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Applying anomaly detection algorithms for streaming network data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND