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Field
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Area: Computer Science 2. Admission Requirements: Graduates (Licenciatura) in computer engineering or related area, with experience in Machine Learning/Deep Learning methods/techniques. 3. Project
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Engineering, Biomedical Engineering (Medical Informatics), or related areas. Recipient category: Masters, enrolled in the course: Degree courses: enrolled in doctorate. Non-conferring degrees courses: enrolled
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the field of the seismic behaviour of masonry structures and machine learning; Have a good proficiency of the English language. At the time of the respective hiring, candidates must prove that
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of the Recovery and Resilience Program, through the Foundation for Science and Technology - FCT, under the following conditions: Scientific Area: Computer Engineering, Biomedical Engineering (Medical Informatics
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Engineering, Biomedical Engineering, Information Systems, Artificial Intelligence or related areas that demonstrate a solid computational base; Candidates enrolled in a non-degree course: Candidates who exceed
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of establishing relationships between signal sources and predicting commands; 6. Design of machine learning and adaptive models that ensure the continuous evolution of the system, increasing the autonomy and
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experience in the fields of HRI, robotics, computer vision, or machine learning. Programming skills. Contracting requirements: Presentation of the academic qualifications and/or diplomas, if applicable
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optional skills and qualifications: Previous research experience, particularly in the fields of Internet of Things security and machine learning model security applied to intrusion detection. Contracting
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 2 months ago
data sharing (preferred); good knowledge of machine learning algorithms; proficiency in high-level programming languages (e.g., C++, Python, C#, etc.) (preferred); knowledge of database formulation and
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sequencing, proteomics, and metabolomics; interpretation of datasets and clinical data using advanced statistical methods and machine learning algorithms to identify correlations between molecular alterations