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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 15 hours ago
of numerical simulations and digital twins. By using advanced machine learning methods, such as Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed Neural Networks (vPINNs), the project
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Charité–Universitätsmedizin Berlin (Dr. Rosanna Sammons); for further information, see https://www.sfb1315.de/ - development of network models of the CA3 region of the hippocampus - investigation
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candidate will work with open available datasets obtained in rodents and unique datasets of neural activity. Your primary focus will be to design new learning frameworks and neural network architectures
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contributions into powerful, integrated systems that drive high-impact publications. Who We're Looking For Solid expertise in deep neural networks, especially using PyTorch Strong interest (or hands-on experience
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research community in exploring near-analog biologic brain inspired solutions to reduce power consumption in neural networks. In this project you will be involved in a collaborative effort investigating
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 14 days ago
) Start to develop trainable Artificial Neural Networks for the identification of sequence patterns relevant for the function of the enhancers that harbor the respective NucAlts. Admission Requirements
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desirable. Familiarity with explainable AI, causal inference, or biologically inspired neural networks, as well as experience collaborating with experimental laboratories, will be considered strong assets
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collaboration with GE Healthcare and their re-search center in Oslo. Your interests are in neural networks research. You have a big motivation to both contribute to new methods in neural networks, and develop
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collaboration with GE Healthcare and their re-search center in Oslo. Your interests are in neural networks research. You have a big motivation to both contribute to new methods in neural networks, and develop
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates