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knowledge and advanced transfer learning techniques. The methodology incorporates fundamental radar wave propagation equations into the diffusion process, allowing for more accurate and physically consistent
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these images. This project proposes an innovative approach that combines state-of-the-art diffusion models with physical radar knowledge and advanced transfer learning techniques. The methodology incorporates
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. Familiarity with frameworks such as TensorFlow and Keras, as well as libraries including Scikit-learn, NumPy, and pandas; - Experience with machine learning models such as Extreme Learning Machine (ELM
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data (PET, CT, Magnetic Resonance Imaging with Late Gadolinium Enhancement – MRI-LGE) and clinical variables. The approach encompasses unsupervised multimodal registration, three-dimensional deep
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; • Electrical characterization through current–voltage (I–V) and capacitance–voltage (C–V) measurements at deep cryogenic temperatures (< 4 K); • Optical characterization by photoluminescence (PL) spectroscopy
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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. Mandatory requirements: PhD obtained within the last seven years in a field related to the project; availability to start immediately; prior publications related to the project; proven skills in MariaDB
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). Candidates must meet the following requirements: - PhD in Computer Science; - Experience in research on the use of AI to recommend code refactoring opportunities; - Experience with data analysis and data
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Immunodeficiencies), Faculty of Medicine (FM), University of São Paulo (USP), under the supervision of Prof. Dr. Maria Notomi Sato. Requirements: PhD in Immunology or a related field. Knowledge of virology, cellular
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learning and community engagement in conservation. Requirements: PhD completed; fluency in English; experience with qualitative methods; experience with and availability for fieldwork, in accordance with