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analyses. Machine learning for biological data (e.g., protein language models, transformers, generative models) and interest in building interpretable tools for experimental colleagues. Qualifications PhD
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outcomes. Key Responsibilities Develop, implement, and optimise AI/ML models (artificial intelligence/classical machine learning, deep learning, computer vision, NLP, etc.) Work with structured and
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, mathematics, computer science, engineering or a related discipline Required Other None Additional Preferred Experience working in one of the following areas: Machine learning/predictive modeling
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Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 days ago
and fine-grained semantic information within the prompts, and assess geometric accuracy of corresponding models' answers. If necessary, we will then propose dedicated learning strategies for inducing
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of the successful candidate. (1) Develop multisource, frugal downscaling approaches. Most downscaling approaches presented in the scientific literature are Machine Learning (ML)-based. The proposing team's experience
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for showcasing the improved mapping and monitoring of forest traits and uncertainties. You will be mainly in charge of: Develop improved hybrid model inversion methods with a focus on machine learning and deep
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, the wider university and occasionally members of the public. If this sounds like you, we’d love to hear from you! Apply now by clicking on the 'Apply' button. Learn more about working in CAR here: https
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models remain a limiting factor in moving to a quantitative scale. Molecular simulation has benefited from recent advances in machine learning and generative artificial intelligence to such an extent
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
abiotic resources. We integrate remotely sensed information with in-situ data, process-based models, and leverage satellite communication, IoT and machine learning technologies in order to provide evidence