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Field
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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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(Postgres, MySQL) and NoSQL (MongoDB, Accumulo) databases Apply advanced data mining, statistical modeling, graph algorithms, and machine learning to extract insights from large structured and unstructured
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algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a sustainable future. These EMS
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learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a sustainable future
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time. The crowdsource-based framework, i.e. a mobile app for data capture and a web mapping application for upload of GNSS raw data, will allow the project to have well-distributed data both in space and
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support to faculty and administrative staff. Continuously monitors OIRDS applications and program scripts to distribute workloads efficiently across servers, ensuring optimal system performance. Implements
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algorithms, and prototypical systems controlling complex energy systems like buildings, electricity distribution grids and thermal systems for a sustainable future. These systems coordinate distributed
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Statistical Interference/Analysis/Modeling, Machine learning and Natural Language Processing, Artificial Intelligence, Algorithms, Data Mining and Modeling, and Big Data Query. Design, coordinate and execute
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The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65
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manipulation detection. Another focus is the development of algorithms for the areas of virtual product development, intelligent actuator-sensor systems and audio for the automotive sector. There are currently