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Field
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based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored, and the findings of your project will be
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hardware modification. The AI will learn and adapt the realms of the combustion modes and fine tune the performance for each while the engine is operated. Self-tuning, adaptive, control algorithms will be
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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing
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analysis, AI algorithm modeling, testing, and integration into functional systems within the project scope. Specifically, in activities related to behavior modeling from IoT device data, generative AI
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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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-on monitoring with cutting edge data-driven and physical based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored
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vision to reduce algorithmic complexity by orders of magnitude, e.g. by tracing paths of trees and extraction from knowledge bases (KBs), as opposed to pure DL Defining specific CSK-premises (in
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, resource efficient algorithms, and programming paradigms for enabling an application-tailored design of dependable communication and computation systems. Project description This PhD project is linked
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Researcher, of the FCiências.ID Scientific Research Career, within the scope of the project HOFGA: The Hardness of Finding Good Algorithms (Ref. HORIZON-ERC-STG-101041696), financed by the European Union´s
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Computing (e.g., memristor modeling/simulation/manufacturing) and Edge AI related areas (e.g., AI algorithms, AI accelerator, VLSI). Background Investigation Statement: Prior to hiring, the final candidate(s