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/deploying deep learning models and machine learning applications. Computer skills: Python (PyTorch, TensorFlow), databases (MySQL), 3D Slicer, ITK-SNAP, OpenCarp. Previous experience in research activity in
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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | 11 days ago
numerical models applied to patient-specific cardiac geometries. • Application of machine learning and artificial intelligence techniques to improve data processing and the integration of multimodal
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learning or multi-agent systems. Experience with cloud-native technologies (Docker, Kubernetes) or distributed computing. Experience with efficient neural architectures, scalable model design, or resource
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performance, yet their atomic-scale origin and role in reactivity remain poorly understood. The project addresses this open problem by integrating high-throughput Density Functional Theory, machine-learning
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Artificial Intelligence, Machine Learning, Computer Science, Telecommunications, or equivalent degree. Minimum of 3 years of postdoctoral research experience. Proven experience in various AI methodologies
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: comparative omics, genetic diversity analysis, mathematical modelling, machine learning, and the use of model organisms. Develop transferable skills such as scientific communication, project management
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of next-generation machine learning models applied to the analysis of multichannel temporal signals, with a special focus on sleep medicine. This project will utilize polysomnographic recording databases
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Learning, and Information Retrieval. The following, among others, will be considered highly relevant subjects: Databases, Advanced Databases, Introduction to Databases, Database Modeling, Analytical
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engineering or similar. Knowledge and experience with deep learning models applied in computer vision. Remarkable academic trajectory, validated by a strong record of publications in relevant international
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/interventions, and clinical diagnoses. The post would be suitable for applicants with general interests in AI, machine learning, large language models, foundation models, signal processing, computational