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*• Experience in Python or another programming language (projects, GitHub repositories, courses, scientific use).• Training or experience in machine learning and data science applied to environmental or energy
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practical skills in the field of data science, especially. multimodal data analysis. Experience on image processing (especially on MRI data) via machine learning. Programming skills (e.g., Python
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principled new models and methods, for modern machine learning problems. Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models
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networks, retrieval-augmented generation (RAG), and model fine-tuning. The candidate should have the ability to instruct and mentor master’s students in data science and engineering on effective problem
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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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capable of understanding, learning, and acting in complex, dynamic settings. The lab’s work lies at the intersection of computer vision, multimodal learning, and robotics, advancing next-generation embodied
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A.I. and machine‑learning techniques where appropriate to improve forecasting, modeling, or analytical efficiency. Utilize Bloomberg or FactSet, including APIs, to support research and analysis. What
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transportation systems and autonomous driving. • Strong understanding of generative AI, deep learning, and multimodal machine learning, with hands-on experience. • Excellent programming skills and proficiency with
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | about 2 months ago
-scale (~10’s of km2) permafrost thermo-hydrological hybrid twin, to be coupled with state-of-the-art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools