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Field
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with contemporary machine learning methods. We are looking for an ambitious Postdoc who will lead our efforts on the design, implementation, and training of mechanistic models of cell organization. In particular, we
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well as interpretability of scientific foundation models. The rush to build foundation models has led to the development of large machine learning models in Astrophysics, fluid dynamics, biology, weather prediction, solar
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machine learning methods that translate experimental measurements of compound retention and metabolism in bacteria into scalable computational models for drug discovery. The Research Assistant/Associate
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of machine learning researchers, engineers, and domain scientists. This collaborative environment offers a unique opportunity to work on cutting edge AI models, with a focus on generalization and
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: Computational Linguistics, Language Acquisition, Cognitive Modeling, Machine Learning The Department of Linguistics at the University of Michigan invites applications for a one-year Postdoctoral Research Fellow
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computational models for industrial capacity planning, logistics optimization, material flow analysis, and supply chain analysis. Apply artificial intelligence, machine learning, LLMs, and advanced statistical
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science, or a related field; experience with using and building machine learning models, developing and validating computational analysis workflows, and developing circuit models is preferred; excellent
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. Constantinides and Dr Dan Goodman, who each bring leading expertise relevant to this research programme. You will implement models in software frameworks for neural simulation and machine learning, and work with
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knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 11 days ago
) Optimization and parameter identification methods Data-driven modeling and machine learning Physics-Informed Learning (or hybrid modeling approaches) Handling and analysis of large-scale datasets (e.g., mobility