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Assistant Professor position in Computer Science with a Focus on Interdisciplinary Data Science_3...
enable advanced analytics across disciplines. Experience in developing and deploying machine learning solutions is considered an advantage. Experience applying such solutions within digital humanities
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. Qualifications Applicants must hold a PhD or equivalent qualifications in a relevant field, such as Child-Computer Interaction, Human-Computer Interaction, Learning Sciences, Educational Technology, Computer
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student projects). Qualification You should have a background within deep learning, big-data, computer vision, or related fields, as well as experience in in-line process monitoring or similar areas
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machine learning models directly on these edge devices for real-time anomaly detection and identification. You will develop robust signal acquisition and processing pipelines, translate research-grade
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collaboration with the rest of our interdisciplinary team at DTU Construct and Vistacon, particularly the other postdoc position focusing on image analysis using deep learning. [NR1] Dissemination of your
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provide the possibility for the student to work with LLMs and machine learning. Your competencies Interest in learner centered technology design, in particular how AI systems can scaffold reflection, agency
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data management and machine learning is also preferred. An interest in energy system topics such as the green transition, sustainable energy systems, digital energetics etc. is preferred. Experience
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- Knowledge in programming in Python or R - Familiarity with machine learning or deep learning methods is a plus - Interest in plant genomics, evolutionary biology, or comparative genomics - Proficient in
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motivated candidate with a strong background in statistics and/or machine learning. Areas of particular interest include, but are not limited to: Causal Discovery and Causal Inference Extreme Value Theory
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data