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on information theory, machine learning, and control to analyse how local variability, sensor drift, and platform differences affect both global model performance and human supervisory factors such as workload and
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; profile root and leaf microbiomes using amplicon sequencing; analyse integrated microbiome and phenotyping datasets; contribute to machine-learning models predicting pathogen invasion success and plant
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organizational theory, the learning sciences, digital transformation, digital technologies, human-computer interaction, and related fields. Within the specific field, the PhD student will engage in both research
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, calorimetry, and synchrotron experimental measurement techniques. Knowledge of AI-based and machine-learning methods is also beneficial. For further information about a specific subject see General syllabus
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WDM switches and the fast control to enable novel low latency highly scalable and flat interconnect AI compute clusters. Machine learning clusters and artificial intelligence (AI) training have become
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machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a biological reference point for identifying disease-specific biomarkers. A central part of the
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of mill and production operations. The scientific challenge will be to use the model and machine learning alongside live mill data (temperature, rolling loads etc) to reverse engineer the current
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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would
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system integrates robotics, automated sample handling, sensor networks, imaging systems, cloud computing, and machine-learning-based analytics. The research work at NTNU will focus particularly
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directly with speaker communities to ensure the technology is genuinely useful to them. You will gain deep expertise in machine learning and natural language processing, access to high-performance computing