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Field
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force fields (MLFFs) that combine state-of-the-art equivariant neural network architectures with robust, well-calibrated uncertainty estimates. These models will enable fully automated active learning in
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, is seeking a highly motivated postdoctoral scholar to lead a project investigating neural correlates and predictors of cognitive outcomes from brain lesions, namely lesions from neurosurgical
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, computational mechanics, computer science, applied mathematics or similar Strong experience with deep learning, e.g. PyTorch, JAX, TensorFlow, and probabilistic methods Familiarity with graph neural networks
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Description The Perception and Active Cognition laboratory in the Center
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relevance Profile Interested candidates should meet the following requirements to be eligible for the position: A PhD or Doctoral degree in Energy Systems, Computer Science, Applied Mathematics, or a field
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platforms Contributions to improved environmental information for safe and sustainable Arctic maritime operations Qualifications Required skills and qualifications PhD in geosciences, computational science
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other Duke faculty, or faculty at other academic institutions. Individual will develop and test novel computational models of the neural activity generated by electrical stimulation of the brain. Also
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with an interest in applying advanced methods to questions in neuropsychiatric research. The candidate must have: A PhD in neuroscience, neuroimaging, medicine, computational science, physics, statistics
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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PhD, or an international degree deemed equivalent to a PhD, within the subject of the position, completed no more than three years before the date of employment decision. Under special circumstances