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Field
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Developing custom data models and algorithms for applied research Assisting with database design, development, and testing Supporting the setup and maintenance of data handling systems Ensuring data security
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need
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estimation methods for deep neural networks. A principled Bayesian framework for multimodal uncertainty modeling. Robust learning algorithms under missing modalities and distribution shifts. New uncertainty
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Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis algorithms for critical equipment (e.g
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demand forecasting or behavior modeling Computing & Data Systems Cloud computing (AWS, Azure, GCP) Big data pipelines, distributed computing, and geospatial data processing Python, R, SQL/NoSQL
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energy resources. The expected outcomes include technical advancement of distributed algorithms for managing energy resources at customer premises. The benefits include more resilient, secure, private, and
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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. Wallace (1996). MML estimation of the parameters of the spherical Fisher Distribution. In S. Arikawa and A. K. Sharma (eds.), Proc. 7th International Workshop on Algorithmic Learning Theory (ALT'96
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to research, develop, and evaluate algorithms for a variety of geospatial, signal, and/or image processing applications. The successful candidate will have a solid background in computer science with strong
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, will allow the project to have well-distributed data both in space and time. This will ultimately lead to higher quality (more spatially and temporally accurate, complete, precise) 3D models. However due