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starting the PhD). The candidate must be qualified for admission to the ph.d. program Strong background in quantitative methods (reflected in courses and/or research experience) Proficiency in R, Python
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, AquaCrop) or experience with agricultural or environmental modelling. Some experience with programming in R and/or Python. Exposure to climate or weather data, forecasting systems, or geospatial tools
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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
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in modelling using Matlab or Python. You have solid experience using process simulator AspenPlus or Hysys, and you have used them to simulate absorption processes. You have experience with absorption
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proficiency in R or Python for data analysis and modeling. Familiarity with analyzing large-scale healthcare datasets and real-world data. Experience in developing and applying simulation models, including
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network analysis) and AI; Experience with programming in languages relevant to designing AI models and social network analysis, at least including Python; Excellent writing skills and proficiency
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quality models, especially for coastal water environments You must be highly proficient in the use of programming languages including, but not limited to python, C++, but also database management
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research in TRL forward. Theoretical knowledge of, or experience with, machine learning such as representation and generative learning, and natural language processing. Programming skills, e.g. Python, Java
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/or human-computer interaction. Programming skills, e.g. Python, Java, or C++. Excellent command in English, verbal and written. Prior experience as a research assistant during (under)graduate studies
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/robotic systems Ability to implement control and kinematics with hardware-in-the–loop Background with relevant packages, (MATLAB, SolidWorks/Creo, ROS/ OpenCV/ python, LabVIEW/C languages) A driven