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surrogates or approximators, such as random forests or shallow neural networks, trained to mimic the outputs of the original computations at a fraction of the cost. This hybridization aims not only
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molecular simulations, and cutting-edge AI techniques including graph neural networks (GNNs) and large language models (LLMs) to accelerate experimental design and discovery of novel materials. The research
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Description Completion of doctoral thesis related to: Process and analyze experimental data. Develop predictive models using deep learning. Train, validate, and optimize neural networks (CNNs, etc.) applied
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A post-doctoral Research Associate position in Neurocomputation is available to work with Prof Zoe Kourtzi at the Adaptive Brain Lab, Univ of Cambridge, UK (http://www.abg.psychol.cam.ac.uk ). Are
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to the following: SDSC6012 – Time Series and Recurrent Neural Networks SDSC6016 – Predictive Analytics and Financial Applications SDSC8013 – Statistical Methods for Categorical Data Analysis (For detailed course
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personalized psychiatry, network science, and recovery-oriented research; Interest in integrating neural, behavioral, and recovery-related outcomes; Excellent communication skills and the ability to work in
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meetings to receive and provide feedback on scientific progress. Qualifications · A Ph.D. in mathematics, applied mathematics, data science, computer science, or a related field. · Experience training neural
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research, all with 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
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brings a strong combination of academic and industry experience. They will possess deep, hands-on expertise in modern AI architectures including convolutional neural networks, transformers, and diffusion
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Part-Time Lecturer for EE7207 Neural Networks and Deep Learning The School of Electrical and Electronic Engineering (EEE) at Nanyang Technological University, Singapore, invites qualified candidates