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engineering service offerings. Other necessary skills: MLOps Experience: Demonstrated experience in operationalizing and maintaining machine learning models in production environments, including deployment
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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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that preserves object identity or style. They should have a solid publication record in top-tier computer vision conferences such as CVPR, ICCV, or ECCV, and demonstrate proficiency in deep learning frameworks
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familiar with data science and machine/deep learning toolkits. Experience with model deployment and the usage of MLOps tools (Dockerization, CI/CD pipelines, edge infrastructure, etc.) is a plus. As a PhD
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competency models in collaboration with content experts. Develops and analyzes the Learning Needs Assessment for the department and clinical areas. Monitors compliance related to mandatory staff education
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competency models in collaboration with content experts. Develops and analyzes the Learning Needs Assessment for the department and clinical areas. Monitors compliance related to mandatory staff education
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, and machine learning experience; and (c) some research publication experience. Knowledge of Arabic language is preferred, but not required. Previous work on Arabic NLP is preferred but not required
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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
strong machine learning and numerical modelling background to add knowledge on the impact of geological heterogeneity and subsurface environments (e.g., depth, exhumation, temperature, pressure) to de-risk
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models, artificial intelligence, Bayesian models, data visualization, dynamic causal models, dynamic systems models, item response theory, large language models, machine learning, mixture models
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of physics- informed machine learning and deep learning, with applications to inverse problems in scientific imaging and the modeling of complex physical systems. The overall goal is to integrate the knowledge