-
involve system integration and calibration, acquisition of spectral maps, and development of tools for data processing and analysis. The researcher will receive training in photonic instrumentation, laser
-
(pre-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models; integration and analysis
-
platforms, employing numerical simulation techniques and experimental analysis. The researcher will contribute to the implementation and preparation of innovation and technological development projects
-
extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models for physiological signal analysis, with a focus on cardiology
-
ATE_SOUND3D_OS funded by IAPMEI with reference 56 Co-financed by Component 5 - Capitalization and Business Innovation, integrated in the Resilience Dimension of the Recovery and Resilience Plan within the scope
-
for testing and validating technologies in an ocean environment. Objectives: • Analysis of key public policies and relevant EU and national instruments for the development of the ocean-technology sector, and