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Field
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strong background in Artificial Intelligence (AI), particularly in the development and application of Large Language Models (LLMs), to join our team working on predictive maintenance solutions. The ideal
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natural language processing with causal estimation. Recent directions in the project include using large language models to remove treatment-predictive information from text, benchmarking debiasing
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Announcement - Mohammed VI Polytechnic University (UM6P), AgroBioSciences (AgBS) Job Title: Post-Doctoral in Modeling and Crop Yield Prediction in Africa Area of specialization: Agronomy, Modeling, biostatistics
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focused on Artificial Intelligence (AI)-driven retrosynthesis and reaction prediction. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic
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generation, targeted atomistic simulations, structural descriptor extraction, and predictive models. The work will aim to establish links between local pore geometry, structural disorder, sodium adsorption
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we collaborate with universities and institutes in Europe, Asia, and North America. More information about the Division of Heat Transfer can be found here: https://www.ht.energy.lth.se/english/ COMPEL
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validating uncertainty-aware AI models for real-time crash risk prediction with guaranteed confidence bounds, interpretability, and fairness-by-design, using a blend of centralized and federated learning
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Potentials (MLIP), machine learning (ML) predictive models and AI tools. Activities : Computer science implying ML and AI tools applied to material science Where to apply Website https://umontpellier.nous
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provide meaningful explanations of their predictions to downstream users. A central aim of this project is to investigate how Vision-Language Models (VLMs), Multimodal Large Language Models (MLLMs), and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 8 days ago
offers hands-on experience with operational satellite retrieval algorithms, physics-based forward modeling, and mission-scale validation practice, complementing the participant's background and advancing