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place to study and work. Postdoctoral Research Fellow position within Physics-Informed Machine Learning for Offshore Wind At the Department of Mathematics , there is a vacancy for a postdoctoral research
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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-computer interaction and design; automated self-driving laboratories that pair hardware instrumentation with active-learning machine learning for experiment design to accelerate chemistry, materials, and
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-driven approach for optimizing the growth of semiconductor materials by combining machine learning with a physics-based understanding of the growth process. Doping and processing of ultra-wide bandgap
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. Project description The PhD fellow will be affiliated with the Scientific Computing and Machine Learning (SCML) research group at the Department of Informatics and supervised by associate professor Anne
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. Required qualifications include a PhD or equivalent degree with exceptional expertise in machine learning as well as postdoctoral qualifications and teaching experience equivalent to the requirements of a
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to their work. About You We encourage applications from individuals with a wide range of backgrounds and experiences. You should demonstrate: Essential Criteria: PhD (or near completion) in engineering, maths
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, Django, FastAPI, or Streamlit) Experience deploying applications to cloud platforms (e.g., AWS, Azure, or GCP) Experience with API integration and working with external data sources Desirable Criteria: PhD