413 machine-learning-"https:" "https:" "https:" "https:" "https:" Fellowship positions
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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics
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implement new nonlinear iterative solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned
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machine learning. The specific goal is to extend new and existing visualization environments to support efficient and precise annotation of histopathology images using a combination of expert human review
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: N/A Department: Elec, Computer & Bio Engr Campus Location: Job Description Summary: The search will remain open until the position has been filled
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PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning for lung cancer imaging data; - identify and select the appropriate methods for the study in question; - develop
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manufacturing principles. Experience with machine learning methods and integration into hybrid modelling systems Demonstrated ability to clearly communicate research concepts and results in high-quality journal
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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, software applications, record keeping, compliance training, and the principles of scientific study design. Learning both general and specialized research skills that will support advancing your scientific
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Website https://www.inesctec.pt/en/opportunity/AE2025-0532 Requirements Specific Requirements Academic qualifications: Training in Electrical and Computer Engineering. Minimum profile: • Be enrolled in a
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programming such as Python, R, MATLAB, or other similar programs and experience in using simulation/optimisation models and advanced data handling techniques e.g. machine-learning techniques, statistics