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involve developing an approach that uses Knowledge Organization (KO) metadata and ontologies to optimize parallel processing and scheduling policies (via Kubernetes) for Machine Learning tasks. The fellow
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Vector, Vue.js 3, Node.js, Python, and JSON; Natural Language Processing (NLP) applied to collections; implementation of data pipelines using AI models, embeddings (RAG), and OCR; demonstrated knowledge in
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), Multilayer Perceptron (MLP), Autoencoders, Convolutional Neural Networks (CNNs), and Kolmogorov–Arnold Networks (KANs). Desirable knowledge of Gradient Boosting models such as HistGBM, LightGBM, and XGBoost
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dedication to the project. Desirable requirements Have solid knowledge of Statistics (research and teaching). How to apply Applications must be sent to artificial.insecurity@gmail.com . Required documents
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the environment, including: i) Knowledge of programming languages (e.g., Python and/or R); ii) Ability to work in an interdisciplinary team; iii) Good oral and written communication skills; iv) Strong
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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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on CV evaluation, will be contacted by email by April 13, 2026, to schedule an online interview. The position is open to Brazilian and international applicants. Proficiency in English and knowledge
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Educational background and field of knowledge: Agricultural Engineering/Agronomy or related fields, with a focus on Plant Pathology. Specific Requirements The candidate must hold a PhD degree with a thesis
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enclosed: - A CV, with links to Google Scholar Research ID, or ORCID profiles; - A letter describing the reasons for the interest in this position; - The names of two persons who are knowledgeable about the
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; - Proven experience in molecular biology techniques, clinical pathology, and translational research with human samples; - Basic knowledge or high motivation to work in bioinformatics and omics data analysis