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, Data Science, AI, Math, Physics, Geoinformatics, or a related quantitative field; applicants from environmental sciences are welcome with strong programming skills (preferably in Python). Experience with
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candidate would have experience with computational modelling and control of dynamical systems. Other useful skills include scientific programming (e.g., Python or Matlab), control system design, and
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-derived spectral signatures. Analytical workflows will be implemented in Python, R, Matlab and QGIS. Classification outputs will be integrated with ecological and spatial data to evaluate biogeographical
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well as data analysis and interpretation. Previous experience in fieldwork, laboratory techniques (chemical analyses), and advanced data analysis will be highly valued. Programming skills in R or Python
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analysis, statistical modelling or remote sensing experience with GIS, programming (R/Python) or handling large datasets demonstrated interest in method development or biodiversity research Great emphasis
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of the candidate Essential requirements: A 1st class or 2.1 degree (or equivalent) in Environmental Science, Remote Sensing, Computer Science, Surveying Engineering, or related field Strong coding skills (Python
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skills (Python, R, Java, …) and interest to work in polyglot software environments Practical experience with machine learning and AI methods and an interest to learn, adapt and apply ML methods
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machine learning tools on the experimental and numerical data. Requirements Master degree in physics, chemistry, mathematics or related fields Knowledge in materials science Knowledge of python good
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equivalent) in physics, computer science, mathematics, data science, computational science, and cognitive science Very good programming skills (e.g., in Python, C++, or comparable languages) Ideally, initial
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, computational science, or cognitive science Very good programming skills (e.g., in Python, C++, or comparable languages) Ideally, initial experience in the field of crowd dynamics, data analysis, or spatial and