140 machine-learning "https:" "https:" "https:" "https:" "https:" positions in Portugal
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river valleys); • Identify stylistic patterns and regional variations in schematic rock art; • Apply machine learning tools for large-scale stylistic classification; • Establish a robust chronological
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of this project for a one-year period (100% full-time commitment) to make a significant contribution to the implementation of machine learning (ML) algorithms. The postdoc is expected to have proven experience in
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physiological signals, with a focus on ECG, and to develop machine learning and deep learning methods for classifying clinical, health, and wellness findings. Supporting project management and research group
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the European Research Council - ERC COG 101088763. The work for this position is in the area of Machine Learning and Natural Language Processing. We are offering We offer a challenging position with the
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of the state of the art in machine learning for generation of artificial data; - identify and select the appropriate methods for the study in question; - develop the research capacity through the application
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 14 days ago
skills. Have very good knowledge of machine-learning and data science methods, especially for timeseries data Have very good programming skills in programming languages such as Python. Have previous
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Python for scientific computing – experience with data analysis and basic signal processing – foundations in machine learning and interest in developing advanced AI models – familiarity with Linux
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program (2025.02832.MAD), funded by FCT-Madeira, under the following conditions: I. Scientific Area: Electrical Engineering and Computer Engineering II. Admission Requirements
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of the Grant are:; 1) To apply machine learning algorithms for the diagnosis of faults and malfunctions in photovoltaic plants, using data from SCADA systems combined with synthetic data from digital twins (DT
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the field of Biological Engineering available within the project “Machine learning-driven CD19 CAR T cell manufacturing for improved safety and efficacy in the treatment of hematologic cancers”, Ref