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Would you like to use large-scale data analysis and machine learning to uncover proteins that conventional approaches overlook? The University of Oslo is recruiting a PhD Research Fellow in bioinformatics
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learning. We are looking for a strong, independent computational researcher who wants to develop original machine learning methods, shape their research direction, and collaborate closely with clinicians
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phase-field simulations. Experience in developing AI or machine learning models for physics-based scientific or engineering applications. Proficiency in Python, scientific computing and machine learning
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of years post-PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW for purposes of collective
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minimum of four years in the Norwegian educational system Strong foundation in computer science or artificial intelligence/machine learning, with knowledge of cybersecurity or a demonstrated motivation to
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experimental optimization, structure–performance analysis, and/or mechanistic investigations. Familiarity with machine learning techniques, optimization algorithms, and data-driven approaches for materials
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proteins · Supervise and instruct graduate and undergraduate students in Dr. Pengfei Li’s lab Minimum Education and/or Work Experience PhD required in related field. Qualifications - A PhD is required in a
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explored to analyse the high-dimensional Raman and autofluorescence datasets. Machine-learning approaches will be used to identify spectral features and molecular signatures associated with infection
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and remote sensing imagery for ecosystem monitoring. Develop machine/deep learning-based workflows to interpret ecosystem disturbance. Synthesize model simulations and multi-source observations
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is developing research excellence in areas including Machine Learning, Big Data, Visual Analytics, Computational Intelligence, Machine Learning, Software Implementation and Testing, and their