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from many different sources into data frames that can be analyzed with biostatistical applications in the statistical software R/Python. Performing data analysis in accordance with time-structures and
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-driven services that can improve the current design procedures in the district energy industry and optimize future operations in 4GDH. Your PhD project will be part of a collaborative R&D effort with
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obtained from Carsten Dam-Hansen and Lars R. Lindvold, DTU Electro and Katrine Qvortrup, DTU Chemistry You can read more about DTU Electro at www.electro.dtu.dk/ and DTU Chemistry at www.kemi.dtu.dk/english
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quantitative methods Experience in one or more programing languages (e.g. R, Python) Knowledge of public health, foodborne disease surveillance, and animal disease surveillance is an advantage Excellent
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suited for data science and statistics: Python, R, MATLAB Strong English communication skills; both written and orally Experience with any of the following is appreciated. mySQL, computational workflow
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rich behavioural data; prior experience in immersive virtual reality is not mandatory. Proficiency in a programming language such as Python, Julia or R for data analysis and model development and
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, MATLAB, R, C++, Julia, potentially HIL. Excellent command of English in speech and writing. Good ability to present results orally, experience with preparing scientific papers for journal publications, and
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, as well as bioinformatic analysis. Programming skills: e.g. R, Unix, Matlab. A strong innovative approach to the research field. Experience, know-how, and vision for integration of scientific findings