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detection is set according to the local statistics of the radar sea clutter. The threshold method works well for large ships due to their high intensity value compared to sea clutter. However, in cases
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mathematical and engineering packages and programming languages for scientific and technical computing, such as Python, Matlab and STK is recommended; knowledge and background in statistics, with a focus on
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knowledge of Machine Learning; Good knowledge of Signal Processing, Data Analysis, Probability and Statistics; Prior Experience in high-level Programming languages such as Python and MATLAB. Diversity, Equity
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statistical analysis; familiarity with Power BI or other data visualisation tools; familiarity with Artificial Intelligence tools; strong organisational skills and attention to detail; ability to work
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; Experience in the use of Rayyan or other software for systematic reviews, Mendeley for citation management and SPSS for data/statistical analysis/machine learning. Diversity, Equity and Inclusiveness ESA is an
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, the adaptation of the ECSS NG to the ESA Mission Classification pre- tailoring activity (both for PA/QA and engineering disciplines) and updating pre-tailoring statistics accordingly. actively contributing
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, particularly those based on Machine Learning supervised classification. Relevant experience includes, but is not limited to, data mining, data fusion, statistics, clustering, signal decomposition/unmixing