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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
Grants to be awarded are divided into four different Research Topics (#1 through #4), having individual Work Programmes, Preference Factors and Minimum Requirements. The applicants must reference, in
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algorithms. The aim is to develop a high-performance intelligent motor control system that enables greater sustainability, safety and efficiency of the e-bike's energy system, in order to meet the requirements
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AND TRAINING: The five Research Initiation Grants to be awarded are divided into five different Research Topics (#1 through #5), having individual Work Programmes, Preference Factors and Minimum
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: ● Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions
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manipulators capable of adjusting their trajectory and resistance in real time in response to variable external loads. This module should integrate learning algorithms based on artificial intelligence, allowing
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. Develop intelligent algorithms that allow cooling systems to be controlled based on sensor inputs, ensuring adaptation to different scenarios and operating conditions; 4. Integrate and validate
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-oriented interfaces is desired.; In parallel, we intend to explore new optimizations, such as data deduplication, support for multi tenancy, and new scheduling algorithms. These optimizations should be
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to explore new optimizations, such as data deduplication, support for multi tenancy, and new scheduling algorithms. These optimizations should be implemented and integrated into the platform.; Implementation
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PRESENTATION OF THE WORK PROGRAMME AND TRAINING: 1. Literature review regarding inspection and quality control methods that are scalable over time and robust to different and new defects; 2. Identification and
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, inferential, and multivariate methods, including principal component analysis (PCA), regression, and machine learning algorithms (e.g., Random Forest), with the aim of integrating various environmental exposure