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Field
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This project is similar in flavour to the Conscious AI project but rather than come from a Philosophical/Neuroscience/Math/Theory angle, this project aims to build self-aware neural networks
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architectures, with a strong focus on spiking neural networks (SNNs). 1 Abstract The increasing availability of spatio-temporal data has enabled significant advances in perception and pattern recognition systems
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20 Jan 2026 Job Information Organisation/Company Scuola Internazionale Superiore di Studi Avanzati Research Field Physics Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 8 Feb 2026 - 23:59...
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RAP opportunity at National Institute of Standards and Technology NIST Leveraging Artificial Neural Networks for Enhancing GC and LC-MS Metabolomics Data Interpretation and Integration Location
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project, “Material Model Discovery from Physics-Encoded Neural Networks”, aims to discover material models that give unprecedented accuracy and computational efficiency, enabling engineers to fully exploit
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comprehensive understanding of core Deep Learning (DL) concepts, techniques, and applications. The ideal candidate will provide expert instruction on neural network architectures, backpropagation, activation
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Teaching Faculty Position - Machine Learning: Deep Learning & Neural Networks: The Brain of AI Mohamed bin Zayed University of Artificial Intelligence: Academic Appointments: Undergraduate Division
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This project focuses on brain network mechanisms underlying anaesthetic-induced loss of consciousness through the application of simultaneous EEG/MEG and neural inference and network analysis
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, custom-trained neural networks, and related tools. Analyze and interpret high-dimensional neural datasets using systems neuroscience approaches such as neural networks, Bayesian inference, and decoding
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-responsive behaviour tasks using tasks that mimic biological learning principles and neuromodulation. Artificial neural networks with biologically-inspired dynamics will be trained and progressively pruned