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computational tools such as Python or MATLAB Excellent team player that can also work independently High degree of reliability, organizational and interpersonal skills Service oriented approach towards researches
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datasets. 1-3 years of experience with crop modeling using gridded datasets Preferred qualifications Strong programming skills in scientific computing languages (e.g., Python, IDL, R, MATLAB, or similar
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/Qualifications Profile - A solid background in continuum mechanics and computational mechanics is required. Advanced knowledge in scientific programming is also requested (e.g. Python, Matlab, possibly FORTRAN
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analytics / atmospheric monitoring of greenhouse gases and interest in automated setups and field work. Knowledge of scientific data analysis (e.g. Python, MATLAB or R). Good verbal and written communication
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analysis using a software platform such as R, Python, MATLAB, STATA, SAS, or others. Proficiency in energy modeling/simulation and cost estimating using such platforms as DesignBuilder and EnergyPlus and
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Python or Matlab. *for students with an education earned outside of Sweden, a 4-year Bachelor’s degree is accepted. The following experience will strengthen your application: Experience in nanotechnology
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are self-driven, eager to learn, and possess good analytical problem-solving skills Programming skills in Python or Matlab. You are expected to be somewhat accustomed to teaching, and to demonstrate
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background in programming and sensor data-acquisition systems Strong programming skills and experience with any of R/MATLAB/Python/C++ Excellent writing and communication skills Working Conditions / Work
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detailed documentation. • Develop models and implement program code (STATA, Python, SQL, R, SAS, Matlab, etc.). • Perform statistical analysis, including regression analysis and machine learning techniques
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programming languages like Python or Matlab environment. Knowledge for programming (python) would be a plus for the force curves interpretation since some codes have been developed for the 3D -AFM data analysis