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: Collaborate and participate in large consortia to assess needs and requirements Design and develop machine learning models for genomics applications Maintain and update existing deep learning frameworks
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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research threads in Computer Vision and Machine Learning : Improving and creating state-of-the-art foundation models to be able to enhance both performance and computational efficiency; Design world models
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 10 days ago
to intertwine a multi-contact whole-body controller, a digital simulation of the interacting humans, and machine learning models to predict and respond to human movements and intentions. In a crescendo of
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analysis Large language models or machine learning/predictive modeling for longitudinal data analysis Strong computer programming skills Strong mathematical or statistical skills Ability to work as a part of
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on artificial intelligence techniques, namely machine learning and deep learning; (3) analysing mathematical models applicable to renewable energy generation technologies and electrical energy storage systems; (4
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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Proficiency in at least one programming language, preferably Python; experience with scientific computing, numerical modeling, or machine-learning frameworks is an asset Strong analytical skills with a solid
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environment where machine learning meets real-world scientific impact. What You’ll Do: Conduct cutting-edge research at the intersection of AI and science Develop large-scale deep learning models for scientific
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Transformers). Analysis of existing datasets. Evaluation of the trained models on suitable datasets. What you contribute Good knowledge in the field of machine learning and training neural networks. Good Python