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should have documented background in the following areas: Electrodynamics Data analysis for scientific applications Programming (e.g., Matlab, Python, C, C++) for scientific applications Previous
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academic track record proficiency in programming (preferably in Python), personal characteristics, such as a high level of creativity, thoroughness, and/or a structured approach to problem-solving
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should have documented background in the following areas: Electrodynamics Data analysis for scientific applications Programming (e.g., Matlab, Python, C, C++) for scientific applications Previous
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, including time series analysis and statistics (e.g. mixed effects modelling) Capacity to develop computer code and experience with programming languages (Matlab, Python, R) and geospatial tools (e.g. ArcGIS
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for the project. Have documented programming experience in R, Python or other common programming languages. Have experience of quantitative analysis, computational modelling, bioinformatics, machine learning
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health, biomedical engineering, applied mathematics, physics, or another quantitative field of relevance for the project. Have documented programming experience in R, Python or other common programming
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- experience in programming (e.g. Matlab and/or Python) - experience in fieldwork - proficiency in scientific writing - skills in communicating with people who have different backgrounds Your workplace
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programming capabilities with ample experience from previous projects are a requirement. Strong experience with Python, data analysis, and visualisation techniques is a bonus. The results of this work will be
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. Flexibility and an interest in interdisciplinary research are considered strong assets. Other assessment criteria Experience with programming (e.g. Python, MATLAB, Fortran or similar), numerical modelling
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programming languages (Matlab, Python, R) and geospatial tools (e.g. ArcGIS) Demonstrated skills in handling and analysis of large datasets Consideration will also be given to good collaborative skills, drive