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://www.di.ubi.pt ), under the following conditions: Research Field: Machine Learning/Pattern Recognition Objectives: Foundational Models for Human-Machine Interaction Work plan: The work consists in develop a system
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models for the fire resistance of LSF walls, including the development of machine learning models, experimental testing, and numerical simulations, within the project “FireLSF – Development of Predictive
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Information Management, or related fields; Have basic knowledge of machine learning models in supervised and unsupervised learning tasks (i.e., k-nearest neighbours, Decision Trees, Neural Networks, Logistic
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the numerical modelling of thermal phenomena, with particular emphasis on solidification processes. d) Knowledge on machine learning methods or data-driven modelling approaches applied to materials science or
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programming in C (on microcontrollers and embedded Linux); ii. Machine learning on the Edge. Priority will be given to candidates enrolled in a Master Program related to Embedded Systems or related fields
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problems (e.g., optimisation, simulation, statistical analysis, or applied machine learning). ; - Participation in R&D projects with links to real-world or industrial contexts. ; - Relevant scientific
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layers and data processing ; 2) Development of machine learning algorithms for traffic characterization and damage detection; 3) Development of a toolbox for the automatic data acquisition and
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prototypes, transforming them into useful information to support agronomic decision-making; Apply data processing and machine learning techniques to relevant problems in an agricultural context; Support the
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modelling, data analysis, and optimisation. Experience with machine learning or surrogate modelling techniques applied to offshore engineering problems. Experience in collaboration with the offshore wind and
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Intelligence (AI) algorithms, including Machine Learning (ML) and Deep Learning (DL) techniques, for advanced signal analysis. The work will focus on developing methodologies for the detection, extraction