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(for example, the distance between the sensors) or based on statistical properties of the measured data (for example, the correlation between the measurements of the different sensors) [2]. Graph-based learning
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our OU community can reach their potential. We recognise that different people bring different perspectives, ideas, knowledge, and culture, and that this difference brings great strength. We strive
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 12 days ago
to reconstruct open rose flowers in 3D. The key idea is to learn two neural networks that operate on different scales. The first network operates on the scale of the full flower to identify the flower architecture
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asynchronous AI-led chemical optimisation across chemistry laboratories¿. This role sits at the intersection of robotics, machine learning, and chemistry, aiming to develop robotic systems that work
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. The appointees will participate in a multidisciplinary collaborative research project related to development of deep learning model for diagnosis and prognosis of different sarcomas. He/she will develop and train
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arboviruses (e.g. dengue, zika), where we investigate how host-virus dynamics affect outcomes in different tissues. By combining computational systems biology with rigorous experimental virology, we transform
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multimodality deep learning model development in different sarcomas. Publications in related fields will be a strong advantage. Opportunities for publication and independent development will be available
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 6 days ago
at the Centre Inria de l'Université de Lille in the Scool team. He or she will be in contact with experts of sequential decision making. The candidate will study different research questions related
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well as interpretability of scientific foundation models. The rush to build foundation models has led to the development of large machine learning models in Astrophysics, fluid dynamics, biology, weather prediction, solar
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applying machine learning and computational methods for protein design, in close integration with experimental enzymology and biocatalysis. The tasks include: Development and application of AI and machine