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environment spanning the Computational Systems Biology group, the Finnish Centre for Artificial Intelligence (FCAI), and ELLIS Institute Finland. You will be supervised by Dr Julien Martinelli and Associate
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and cooperation. You have a strong interest in machine learning/artificial intelligence. You have a strong interest in graph-based learning (e.g., graph neural networks). You have experience with deep
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al., “Deep Transfer Learning for Fault Diagnosis”, IEEE Transactions on Industrial Electronics, 2020. • Zhang C. et al., “Graph Neural Networks for Power Systems”, Electric Power Systems Research, 2023
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on developing novel neural network approaches to predict protein conformational dynamics from fixed protein structures, addressing fundamental challenges in structural biology with broad applications in
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neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view learning, transfer learning, and data fusion techniques to integrate heterogeneous omics datasets
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recognize that artificial intelligence and machine learning are changing all aspects of neuroscience from analysis of neural activity to network modeling to imaging to bioinformatics to molecular modeling. We
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
following areas: High-dimensional probability and concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations
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Technologies (Ireland); and a network-wide training program. Responsibilities and qualifications The PhD scholarship is on the topic of “Carbon-Aware Neural Architecture Search (NAS)”. Most AI models are built
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Systems Administrator - Center for Neural Science US-NY-New York Job ID: 2026-15890 Type: Arts and Science (AS1111) # of Openings: 1 Category: Technology New York University Overview Arts & Science is
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of innovative AI approaches for mental health research by designing neural networks and large language models for difficult-to-treat depression. You will contribute to the design of research materials and data