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discrete black box combinatorial optimization problems (https://arxiv.org/abs/2510.01824 ). In this work, we parameterize a multivariate autoregressive generative model for generating solutions. By sampling
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depositing and fabricating thermo-electric devices onto non-planar metallic substrates. In a parallel track, the team will develop techniques for the design and manufacture of the structures with predictable
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, to decipher the early mechanisms of tumor transformation. We will leverage innovative models to investigate the early stages of tumor transformationin the context of brain development. Specifically, we will use
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large-scale proteomic, transcriptomic, and perturbational datasets to build predictive and interpretable models of biological systems. The role will focus on developing computational frameworks
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 21 days ago
/Administrative Internal Number: 25565 POSTDOCTORAL ASSOCIATE, McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, to develop computational circuit models, analytical theory, and
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the Sustainability Institute at University College Cork. UCC invites applications for a Postdoctoral Researcher with strong expertise in fault detection, diagnostics (FDD), and preventative/predictive maintenance
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programs-ranging from national infrastructure to major technological rollouts. The project focuses on developing intelligent decision architectures, predictive analytics, and adaptive computational models
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reputation and publication record in consciousness science, psychedelic science, predictive-processing, computational modelling, and integrative theoretical work. You will have a proven track record of
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research communities. Applicants are invited to propose a research project around the development of AI models for predicting promising catalyst candidates to integrate molecular modelling techniques
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Veterinary Medicine ● Variant discovery and genome annotation: Apply deep learning and graph-based models to improve variant calling, transcriptome annotation, and functional prediction in veterinary-relevant