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/10.54499/2023.11234.PEX , funded by national funds through FCT/MECI, under the following conditions: Scientific Area: Machine Learning applied in Applied to Fluid Dynamics Simulation Admission requirements
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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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, and visualization, time series processing, and machine learning. Sense of responsibility and ability to communicate and integrate into multidisciplinary work teams. 3. Financial component - According
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clinical data and machine learning algorithms. The main activities include: Data Processing: • Collection of historical patient data (demographics, clinical history, outcomes of interventions). Data cleaning
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clinical data and machine learning algorithms. The main activities include: Support for AI Model Development: • Collaborating on the training of predictive models under the supervision of the scientific team
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with machine learning. Evaluation of the final solution. V - Initial grant duration: 5 months V.I - Renewal Possibility: Possibily renewable VI - Funding and financial conditions of the grant VI.I
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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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Artificial Intelligence, with an emphasis on the development of methodologies and techniques for Evolutionary Computation and Machine Learning, with an emphasis on task allocation and route planning methods
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. They should be able to thrive in the multidisciplinary culture of INESC MN and fit in projects involving physics, microfabrication, machine learning, mechatronics, etc. and be able to communicate