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Field
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viability data to discover new biomarkers and treatment strategies. You will work in a highly interdisciplinary environment spanning oncology, cell biology, imaging, bioinformatics and machine learning, with
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environment spanning oncology, cell biology, imaging, bioinformatics and machine learning, with access to state-of-the-art robotic drug screening and high-content microscopy infrastructure at the NOR-Openscreen
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systems are reshaping how we learn, work, create, lead, and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—humans and machines working and learning together. Our
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intelligence—humans and machines working and learning together. Our mission is to establish an internationally leading interdisciplinary hub that advances foundational research, responsible innovation, robust
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global and regional climate change and are at the core of international community climate modeling efforts that also involve AI and Machine Learning. We study climate risks and impacts on nature and
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methods in forestry generate vast quantities of data and demand more accurate information. Machine learning allows for the systematization and processing of this data into new forms of information
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– the Norwegian Centre for Knowledge-driven Machine Learning. We are looking for a motivated candidate, who has interest in both theoretical, methodological and applied research in anomaly detection in sequential
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to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field
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, bioinformatics, information security, machine learning, optimization, programming theory, visualization, and didactics. Affiliated centers and labs include the Center for Data Science (CEDAS) , the Computational
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research and academic bodies. This collaboration is centered around a unique, open-source digital platform enriched with data and powered by domain knowledge-based advanced machine learning and artificial