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Faculdade de Ciências Médicas|NOVA Medical School da Universidade NOVA de Lisboa. | Portugal | 2 days ago
supervision of the project’s Principal Investigators: Create a spectra library of arginine methylation peptides Train transformer models to predict MSMS spectra of arginine methylation peptides. Place of work
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and trustworthy machine learning-based clinical prediction models. Funded by the Medical Research Council (MRC) and the National Institute for Health and Care Research (NIHR), the project aims
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, we aim to create autonomous “self-driving” microscopes that: build statistical models of biological dynamics in real time predict the most informative next experiment execute it automatically on living
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deep learning models to predict and analyze large-scale orbital capability. - Evaluate and optimize the performance of the models, comparing them with traditional orbital analysis methods. Where to apply
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pain, a critical and currently missing component in translational research. These new models are intended to enable accurate prediction of analgesic efficacy and disease-modifying effects of novel
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creating a unified data framework for microbial carbon dioxide conversion and establishing a predictive AI modeling. Your profile The candidate is required to have a strong background in AI/machine learning
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, computational pathology, and spatially resolved multi-omics data. The system will leverage generative models like diffusion models and Variational Autoencoders (VAEs) to simulate phenomena and predict outcomes. A
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development of model predictive control algorithms for autonomous robots. Key Responsibilities: Development of model predictive control algorithms for autonomous robots Job Requirements: A Master degree in
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., SHAP, LIME) and radiomics preferred. Quantitative Analysis: Demonstrated ability to handle multimodal datasets, conduct statistical analysis, and apply predictive modeling and validation techniques
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engineering for mobility platforms • AI/ML for transportation prediction, system optimization, and environmental/health impact modeling • Deployment of decision-support tools for public-sector clients