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Field
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graph analytics, including: Graph modeling and analysis using tools such as NetworkX Graph-based ML or graph neural networks (GNNs) is a plus Deep understanding of PostgreSQL/PostGIS, geospatial analytics
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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: Research and tool development at the intersection of artificial intelligence and scientific computing including physics-informed neural networks and digital twins; uncertainty quantification, high
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(e.g. extreme value analysis) to identify patterns of marine extremes and their spatial and temporal characteristics; developing machine and deep learning models (e.g. convolutional neural networks
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, focusing on AI- based quantitative imaging of human hemodynamics, as well as neural network models of nonlinear ultrasound for imaging and quantitative tissue property estimation. The outcome of
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consider strong candidates in any research area but will prioritize Artificial Neural Networks. A PhD in computer science or a related area is required. The successful candidate will receive a competitive
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the strategic direction of research and education in Neurosymbolic Artificial Intelligence. You will drive high-impact research with national and international visibility across areas such as the integration
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constructs, to study neural network dynamics, disease mechanisms, or drug response using in vitro or ex vivo systems. The candidate will collaborate with neuroscientists, stem cell researchers, and
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in AI (machine learning, neural networks, reinforcement learning, dynamic modelling and/or Bayesian inference). You have experience in software development with solid knowledge of one or more
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relevant technical software, such as Cadence, SPICE, Verilog, etc. are needed; • Background knowledge in neural network algorithms preferred, but not required • Collaborative skills, student mentorship