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- NTNU - Norwegian University of Science and Technology
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Field
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analysis. PLEASE NOTE: For detailed information about what the application must contain, see paragraph “About the application”. The appointment is to be made in accordance with NTNUs guidelines
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partners (schools, public and private sectors) and engage with the analysis of the collected data. This main goal of this particular PhD fellowship is to explore how humans interact with the automatically
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), Deep Neural Networks. Probabilistic Machine Learning and Time-series Analysis. Industrial applications of AI (energy, process industry, automation). Software development experience in teams. Programming
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familiarity with laboratory methods, such as qPCR, immunohistochemistry and flow cytometry are required. Experience in use of omics tools and handling of big dataset as well as analysis of data using R software
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statistical analysis and ecological data, preferably using R or related tools Good written and oral communication skills in English, and knowledge of a Scandinavian language will be an advantage In addition
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profiling, and bioinformatic analysis. More about the position The main purpose of the fellowship is research training leading to the successful completion of a Ph.D. degree. The duration of the appointment
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educational background and completed research work. Your application must also contain a plan for your doctoral project which includes: project description plan for the training component progress plan funding
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interest for good practice in programming, data management, and data analysis. Emphasis will be placed on personal qualities. We offer An exciting job with an important mission in society. Developing tasks
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interest for good practice in programming, data management, and data analysis. Emphasis will be placed on personal qualities. We offer An exciting job with an important mission in society. Developing tasks
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on quantitative analysis, design of algorithms, and proofs of algorithm properties. Importantly, the successful candidate has a strong interest in exploring both theoretical and practical aspects of differentially