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Field
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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Are you eager to support exciting research with ultracold atoms and trapped ions? Do you want to participate in the creation of complex machines that have never existed before? Do you enjoy sharing
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) Developing machine-learning based exoskeleton controllers to work across tasks 2) Designing and validating new robotic lower-limb prostheses 3) Exploring other high-risk high-reward research areas related
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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to design and implement machine learning techniques and algorithms. * Demonstrated expertise in the Linux computing environment. * Excellent planning, organization and execution skills. * Positive attitude
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. Specifically, the PhD candidate is expected to contribute corpora preparation (collection and organizing the annotation), use machine learning approaches for irony detection, and testing for experimental and
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+to+apply#Howtoapply-Eligibility) a Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, Cognitive Science, Psychology or a related field excellent knowledge in AI and at least one
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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strong research capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives
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control subjects based on diffusion MRI images and functional MRI responses. Duties include: Developing machine-learning and/or deep learning pipelines for classifying patients of optic neuropathies and