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or related field;* Solid knowledge in machine vision and deep learning (e.g., TensorFlow, PyTorch, OpenCV); Experience in Python programming (focus on libraries for data analysis and AI); Previous
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analysing the influence of the main machining parameters on the dynamic behaviour of cutting, with the objective of identifying instability conditions and supporting process optimization. This work plan is
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within the framework of project PROMisH - Potential of Rheologically-Optimized Magnetic Emulsions in Hemotherapy, with nº 16207, and the operation code at the Funds Platform COMPETE2030-FEDER-00736400 and
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de Campos IV - Work Plan / Goals to be achieved: Activity 1 – Optimization of the rubber solution formulation and application This activity involves improving the properties of the dissolved rubber
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samples. The candidate will be involved in developing, optimizing, and validating advanced analytical methodologies, applying them to analyze environmental samples, processing and interpreting data using
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relationships, this project aims to elucidate the mechanisms governing hydrogen uptake and release, ultimately contributing to the rational optimization of materials for efficient, safe, and controllable hydrogen
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have: (i) preferably a background in or an interest in sustainability, biocatalysis, or sustainable chemical processes; (ii) preferably general knowledge of or prior exposure to chemical and thermal
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requirements: Candidates should possess expertise in the characterization of nutrients and bioactive compounds derived from undervalued resources and food products. Experience in optimizing extraction processes
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than 14/20 (1 point); B. Knowledge of Interactive Systems Design, Cyber-Physical Systems, Predictive Maintenance Systems, Automation, Machine Learning and Artificial Intelligence, Sensor Networks
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Escola Superior de Design, Gestão e Tecnologias da Produção de Aveiro - Norte da Universidade de Aveiro | Portugal | 2 months ago
Regulations of the University of Aveiro. 5. Work Plan: This project aims to develop solutions based on Artificial Intelligence for optimizing additive manufacturing processes. Machine learning techniques will