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knowledge of C++, Python, and R. Who we are At the Department of Agroecology, our main goal is to contribute to sustainable solutions to some of the world’s biggest problems within the areas of soil, plants
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programming skills (R, Python, or Matlab), particularly regarding bioinformatic tools. In addition, you are expected to have insight into plant biomass composition, chemistry and analytics, some practical
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mountain glaciers), with proficiency in MATLAB/Python/Fortran, and related software tools Experience with satellite remote sensing, including both optical and radar techniques You must have expertise in data
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will include: Integrating new scientific methods into our Fortran and Python-based tools, and verification and validation of the resultant implementations Supporting large-scale geospatial global and
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Programming skills (particularly in C++ or Python) Simulation Experience with automation hardware and software. Strong mathematics skills and practical knowledge in 3D modelling. Preferably, the candidates have
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programming skills in Python are essential, along with the ability to design experiments, analyze results, and interpret findings. Knowledge of optimization techniques, statistical modeling, and data management
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: Utilize Python, R, SQL, and containerization (Docker, Kubernetes) to run data ingestion and processing within automated data pipelines Optimize data processing workflows, reducing manual intervention
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stronginterest and experience with GIS data and tools for urban mobility with someprogrammingskills of Python/R, JavaScript, database management environments, Geographical AI and machine learning workflows
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and Computer Vision Excellent programmer in Java / C / Python or equivalent Excellent at using Machine Learning software, e.g. PyTorch / TensorFlow / Scikit Learn Highly knowledgeable in mathematical
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for Intelligence analysis. Implement Agent-based Modeling tools in Python. Develop and implement novel tools for Intelligence analysis. Carry out benchmark analysis, evaluating improvements over traditional