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architectures and principles from Bayesian neural networks and biological sequence models, including large DNA and protein language models. The project also aims to develop a prototype federated learning
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
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interaction networks that contribute to the pathogenesis of these diseases. This is a full-time, non-tenure-track position working in the Laboratory of Molecular Therapeutics. The appointment is annually
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-edge solutions and pushing the boundaries in the field Develop advanced artificial neural networks (ANN), including training, mapping, and weight quantization Collaborate with cross-functional teams
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-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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to machine learning and deep neural networks, into the DG finite element solver to reduce computational costs while maintaining the accuracy. The key objective of this work will be to provide step-change
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interested in conducting mathematical research that combines its theoretical rigor and beauty with real-world applications, including climate and weather, the formation of opinions and neural networks
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the Spiking Neural Network (SNN) itself. However, close collaboration with another PhD student working on the SNN hardware design is expected to ensure seamless signal interfacing and system integration. Key
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? We invite applications for a PhD position, focusing on the design and implementation of Spiking Neural Networks (SNNs) using CMOS technology. Project Overview This PhD position is part of a