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development plan, specifying career goals and the competencies that the PhD fellow should acquire, no later than one month after commencement of the fellowship period. The department is responsible for ensuring
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of Medicine We focus broadly on quantitative and machine learning techniques in multiple modalities of medical imaging (e.g. fundoscopy images, OCT scans, MRI, CT, X-ray and digital pathology). We bridge
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-fellow-in-deep-learning-for-imaging Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/287578/phd-research-fellow-in-de… Requirements Research FieldComputer scienceEducation LevelMaster
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large, highly diverse and multi-modal datasets (e.g., images, surveys, statistical and sensor data). Familiarity with geostatistical, GDAL, Python, PostGIS/PostgresSQL, Machine Learning, AI, Internet
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research independence, the capacity to support junior team members, and strong communication
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necessary lead relevant meetings. To undertake any other duties relevant to the programme of research. Job Requirements: PhD degree in Computer Engineering, Computer Science, Electronics Engineering or
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of hire. MD or PhD. Research expertise in Ophthalmology, Biomedical Engineering, Computer Science, Data Science, Machine Learning One year’s work experience, or equivalent in thesis research and other
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data to address priority questions in cancer care pathways, diagnostic delay, and treatment access. The role will involve advanced quantitative analyses, such as survival modelling, machine learning, and
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research