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13 (5 points); Bachelor Degree classification lower than 13 (2 points); B. Knowledge of Cyber-physical Systems, Automation, CAN Communication Protocol, Machine Learning, AI, Sensor Networks
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classification lower than 13 (2 points); B. Knowledge of Cyber-physical Systems, Automation, CAN Communication Protocol, Machine Learning, AI, Sensor Networks, Hierarchical Decision and Control Systems with main
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. Implementation of signal detection algorithms and triangulation ; 4. Planning and participating in field tests to evaluate system performance; 5. Reporting and disseminating the work developed (ideally with a
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suppliers and purchase of components; 2. Prototype development and validation in the laboratory; 3. Design of test scenarios using different food formulations provided by project partners; 4. Verification and
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mineral deposits) Preferential factors: (1) Knowledge of geochemistry, in particular, major elements and potentially toxic elements in different geological materials, (2) Analytical experience in
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in different programming languages applied in the field of biology; knowledge of machine learning, biostatistical analysis, database creation and implementation; proficiency in English (written and
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: Simulation of internal combustion engines (particularly the Nissan PSA DV5 engine) using ANSYS Forte software, fueled with 100% B7 diesel and mixtures of B7 diesel with different percentages of methanol
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and multicultural teams; - good interpersonal relations and teamwork skills. 3. Work plan: The selected candidate will participate in different tasks of the project “Dropin@ IPB2.0 - Pathways
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be involved in several different activities in project EquiVet.AI, including: - Development of a comprehensive AI-based diagnostic system for allergic diseases in horses (with respiratory and/or dermal
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to adapt the bending operation to different materials or to adjust the process parameters over time (e.g. depending on tool wear), enabling real-time control and optimization of the bending process. V