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Field
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learning, or human-computer interaction would be advantageous. How to apply We are seeking expressions of interest from qualified domestic candidates who wish to apply for this PhD opportunity. This position
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for probabilistic unsupervised learning for structured biological data. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/307053/3-years-phd-position-in-probabilistic-machine
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welcomed. The project sits at the intersection of statistical genetics, systems biology, and machine learning, with strong emphasis on methodological development. Tasks of the PhD Student - Develop and
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intelligence, computer science, remote sensing, geomatics, data science, or a forest/environmental science discipline with a strong quantitative or AI component Strong knowledge of machine learning and deep
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predictions of river flow direction and connectivity. You will lead the development of scalable and reproducible data and machine-learning pipelines, upgrade the GRIT global hydrography, and design and evaluate
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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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, computational biology, statistics or a closely related field. You have strong programming skills, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision
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fusion, machine learning, and systems modelling. We are at the forefront of method development towards large-scale data analysis and modeling of biological systems. Together with a wide range of
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and machine learning methodology to help deal with key challenges in developing such models in large-scale observational electronic healthcare record data. These models will be applied to important real
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In this position, you will join our Simulation and Data Lab for AI and Machine Learning for Remote Sensing . The lab advances interdisciplinary research and operational services by combining