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Field
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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CBS - Postdoctoral Position: Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient
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-reconstructions and observations, low-order data assimilation, or deep neural networks. A quantification of the impact of mesoscale and submesocale features is also expected. At a later stage, the successful
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, implement, and evaluate computational models that assimilate 2-photon data (60%) Use a computer programming language to create novel neural network simulations (models) that include realistic simulations
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• Skilled in single-cell/population data analysis (e.g., GLMs, decoding) Preferred Qualifications • Background in machine learning or computational modeling (Bayesian methods, neural networks, etc
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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the goal of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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. The central goal of the laboratory is to study the neural mechanisms involving dynamic RNA modifications during cognitive development and decline. To achieve this, research projects rely on the use of a
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, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing ● Emerging AI/ML models and hardware