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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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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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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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architectures for TTS and ASR Entrenamiento de modelos a gran escala utilizando frameworks modernos de deep learning / Training large-scale models using modern deep learning frameworks Publicaciones en
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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
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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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learning workflows, and perform data quality control across multiple datasets. The ideal candidate will implement data science analytical models and machine learning models following established
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using machine learning methodologies; (2) the extension of an existing CFD framework for multiphase modeling to the case of PEC systems; (3) the implementation within the framework of a description of
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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data