10 postdoc-image-processing Postdoctoral positions at UNIVERSITY OF HELSINKI in Finland
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researchers (for further information, please see: https://blogs.helsinki.fi/viikki-postdoc/ ). Application and selection procedure To apply, please send the following documents in a single pdf file: * A letter
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natural language processing techniques to analyze digital content such as images, videos, and textual content that relate to human-nature relationships, with a focus on data derived from digital platforms
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investigation and will develop an advanced computer modeling framework. By simulating processes at various scales, from the atomistic to continuum, we aim to reveal how temperature and saturation fluctuations
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. The position will be filled as soon as possible, or as agreed with the selected candidate. Selection process and employee benefits University of Helsinki welcomes applicants of any gender, linguistic and
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processes contributing to cancer. The candidate We seek a motivated candidate with a strong interest in computational cancer research, who is enthusiastic about applying deep learning methods to cancer data
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regulatory processes contributing to cancer. The candidate We seek a highly motivated candidate with a track record of statistical models, network science, and/or computational tool development dedicated
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from buildings, mobile network data) Database management skills (e.g., PostgreSQL) Statistical expertise related to big data processing and high-performance computing (Python, R) GIS software proficiency
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“Targeting metabolic adaptation to combat difficult-to-treat urinary tract infections caused by E. coli biofilms” led by prof. Päivi Tammela, at the Division of Pharmaceutical Biosciences. The aim
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from buildings, mobile network data) Database management skills (e.g., PostgreSQL) Statistical expertise related to big data processing and high-performance computing (Python, R) GIS software proficiency
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a probationary period. The appointment is in the Research Council of Finland research project “Targeting metabolic adaptation to combat difficult-to-treat urinary tract infections caused by E. coli