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their 4th or 5th year of studies (M1, M2 or gap year) - Computer vision skills - Machine learning skills (deep learning, perception models, generative AI…) - Python proficiency in a deep learning framework
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learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop and train deep learning models (CNNs, ...) data to predict IPLSs
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 13 hours ago
mathematical formalization AI / machine learning / deep learning background some Python / PyTorch experience scientific curiosity, taste and autonomy in explorative tasks and problems References [1] Toussaint
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language processing that address concrete problems and are both theoretically rigorous and interpretable. The PhD is funded by the ERC CoG PANDORA (Deep Multimodal Learning for Mining and Generation of Arguments
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out a rigorous scientific study aimed at comparing the performance of deep learning models in detecting complex visual anomalies. Take charge of the entire study, define the evaluation criteria and
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, visiting researchers, master's students, etc.) Research Context Recent advances in mobile robotics have been driven by remarkable progress in perception, deep learning, and control. However, current robotic
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pipeline engineering Strong proficiency in Python development practices and deep learning frameworks, specifically PyTorch and MONAI Practical knowledge of computer vision, pattern recognition, and image
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with deep-learning, UniGEM aims to build a neural network to estimate epidemiological parameters of P. falciparum, the deadliest malaria parasite species (read [1] to learn more about the ideas behind
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graphics, machine learning and graphics programming, as well as a good command of English. Knowledge of data science and deep learning is an advantage. You demonstrate good team spirit, interpersonal and
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 months ago
Approximations in Simulation-Based Inference*. Accepted at NeurIPS 2023. arXiv:2306.03580 L. Meyer, L. Poittier, A. Ribes, B. Raffin. *Deep Surrogate for Direct Time Fluid Dynamics*. Machine Learning and the