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: Design hierarchical models that explicitly capture misspecifications in metabolic models Develop differentiable and scalable inference algorithms using automatic differentiation Implement HPC-tailored
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language models to whole genome sequencing data - Develop algorithms and neural network architectures for the prediction of structured outputs (i.e. trees, graphs) - Implement and develop methods
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disease patients using radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical
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challenge rather than a coding problem. See website for details of programs: http://www.coe.neu.edu/graduate-school/multidisciplinary Responsibilities: Teach selected graduate courses in the MGEN Cyber
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turbine blades. Successful re-development for end-of-life composites could enable reuse in other structural applications. This PhD will investigate the development of hierarchical Bayesian algorithms
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transmission of information and energy, systems theory, and computational hardware and software. ECE students are encouraged to develop synergies with disciplines outside of engineering. The candidate should
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will develop and evaluate fault detection and fault location algorithms for these systems. The project is funded by GE Vernova under a wider collaboration with Imperial College London. You will be co
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. The PhD will employ a combination of simulation and experimental validation. First, use and develop existing coronagraphic simulation tools in python to develop innovative algorithms, then conduct tests
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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between
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for Research in Mathematical Sciences and the Principles of Intelligence (PrincInt: https://princint.ai/ ), housed within the Fields Institute's Centre for Mathematical AI. The fellowships support research