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Optimizing spectrophotometric sensor calibration for water quality analysis: A machine learning approach
Do you want to be part of an innovative research project that has the potential to make a real-world impact? If so, then we have an exciting opportunity for you. You will work in a multidisciplinary research environment, and you will have the chance to gain valuable experience in machine learning and data analysis.
Keywords: Water quality, Spectroscopy, Chemometric, Machine Learning, Optimization
See attached file.
See attached file.
In this research project, we want to answer two research questions: i) What is the optimal number of reference measurements required to ensure a satisfactory model calibration, and ii) How to incorporate expert knowledge into the calibration process? We will provide access to the data, necessary expert background information, as well as relevant scientific literature on the topic of spectrophotometric sensor calibration. Your goal is to develop, implement and compare different calibration strategies, in order to answer our research questions.
In this research project, we want to answer two research questions: i) What is the optimal number of reference measurements required to ensure a satisfactory model calibration, and ii) How to incorporate expert knowledge into the calibration process? We will provide access to the data, necessary expert background information, as well as relevant scientific literature on the topic of spectrophotometric sensor calibration. Your goal is to develop, implement and compare different calibration strategies, in order to answer our research questions.
Pierre Lechevallier
Pierre.lechevallier@eawag.ch
Dr. Andreas Frömelt
andreas.froemelt@eawag.ch
Nicolas Neuenhofer
Nicolas.Neuenhofer@eawag.ch
Pierre Lechevallier Pierre.lechevallier@eawag.ch Dr. Andreas Frömelt andreas.froemelt@eawag.ch Nicolas Neuenhofer Nicolas.Neuenhofer@eawag.ch