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The postdoctoral researcher will join the "Network Dynamics & Computations" team led by Srdjan Ostojic and develop research projects on modeling neural circuits and their role in behavior. The work will focus
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cell, foundation models, graph neural networks, or multimodal omics integration). Epigenetics (DNA methylation, chromatin analysis, single-cell epigenomics). Stem cell research (iPSC culture
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We are looking for a highly motivated neuroscientist or engineer as a Postdoctoral Researcher to join the Liebscher Lab (https://sysneuro.i-med.ac.at), where we investigate how neural circuit
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at least two of the following areas: AI/machine learning for biological modeling (e.g., virtual cell, foundation models, graph neural networks, or multimodal omics integration). Epigenetics (DNA methylation
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related to staff position within a Research Infrastructure? No Offer Description You will join the GIN: https://neurosciences.univ-grenoble-alpes.fr You will be supervised by Julien BASTIN (Inserm Research
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 12 days ago
to reconstruct open rose flowers in 3D. The key idea is to learn two neural networks that operate on different scales. The first network operates on the scale of the full flower to identify the flower architecture
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interdisciplinary environment spanning explainable AI, causality, knowledge representation, and neural networks. Research (90%) research in probabilistic machine learning and neuro-symbolic AI (e.g. neural nets
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. Indeed, the methods currently used rely on optical image databases of various avalanche observations. A deep neural network was trained on this data to enable automatic avalanche detection FIGURE 1 (a) [1
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implement and train neural network architectures, including Physics-Informed Neural Networks (PINNs), in order to integrate physical constraints into the learning process and improve the identification and
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:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome