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with e-CALLISTO instruments or Software-Defined Radios (SDRs). · Familiarity with machine learning for astrophysical data analysis. · Knowledge of solar radio data pipelines and event classification
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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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engineering Engineering » Computer engineering Engineering » Knowledge engineering Engineering » Simulation engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Portugal
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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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for the analysis of complex experimental data; • Development and implementation of strategies for multiomics data integration and systems biology; • Use of Python and R for statistical analysis, machine learning
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 25 days 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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Operations & Technology Knowledge Center, with the following conditions: MAIN FIELD:………..…………………………………………………………………………………………… Management, with particular focus on the intersection of: Machine Learning Causal
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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
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:………..………………………………………………………………………………………………………………………………………… Management, with particular focus on the intersection of: Machine Learning Causal Inference Applied Field Experimentation Scientific Areas: Management, Economics, Information Systems, Computer Science, Data