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Faculdade de Ciências Médicas|NOVA Medical School da Universidade NOVA de Lisboa. | Portugal | 3 months ago
experience, based on the application documents and in accordance with the following criteria: a) Deep neural networks – 60% Experience, technical knowledge and/or relevant training in the development
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the scope of the project “COIMBRA Sustainable Tourism LLM Large Language Models applied to create better experiences for tourists and local communities, whilst supporting the development of a
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interviews is open to award no. 1 research grant in the field of “Analysis of high-resolution morpho-bathymetric and seismic data for realizing the CARG geological sheet No. 627 “Selinunte” and development
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 3 months ago
learning libraries (e.g., PyTorch, Scikit-learn, Hugging Face Transformers) and tools for efficient software development (e.g., Co-pilot, Cursor). Desirable: Basic knowledge of data analysis or experience
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-Law No. 123/2019, of 28 August; Regulation of Research Grants of NOVA.id.FCT – Association for Innovation and Development of FCT; Regulation of Research Grants of the Foundation for Science and
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Escola Superior de Design, Gestão e Tecnologias da Produção de Aveiro - Norte da Universidade de Aveiro | Portugal | 3 months ago
Development Fund – FEDER, through the Innovation and Digital Transition Program – PITD. 3. Admission Requirements: Master in the field of Materials Engineering, or Chemical Engineering, or Polymers Engineering
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applicable law. 3. Work plan: The selected candidate will work out R&D on ML-based Perception for AMRs targeting industrial applications. The work plan comprises the following tasks: - Development of a
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to the following tasks (WPs): WP2: Design and development of permanent magnet assembly WP4: Development of a magnetic refrigerator with 10 K temperature span and 100 W cooling power WP5: Testing and assessment
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training through the development of research work leading to the respective academic degree, whether or not integrated into R&D projects. Are intended for carrying out R&D activities by graduates enrolled in
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preprocessing; Integration of geospatial and soil data into databases. Data processing and analysis: Application of geoprocessing and spatial analysis techniques; Development and calibration of predictive models