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researcher. The project aims to develop foundation models that can learn from heterogeneous tabular data across buildings and district-scale energy systems and transfer across systems, operating conditions
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to develop foundation models that can learn from heterogeneous tabular data across buildings and district-scale energy systems and transfer across systems, operating conditions, and downstream tasks
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datasets with multi-omics profiles of tumors from previous studies Developing new machine learning models through collaboration with the Swiss Data Science Centre Establishing data analysis pipelines
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scalable, cost-effective processes to efficiently remove atmospheric CO2. High-temperature ammonia separation: Creating novel high temperature separation technologies to make ammonia synthesis significantly