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include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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Environmental Engineering or a related field High proficiency in programming languages (e.g., Python or MATLAB) A strong record of authorship in peer-reviewed academic journals Excellent interpersonal and
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Environmental Engineering or a related field High proficiency in programming languages (e.g., Python or MATLAB) A strong record of authorship in peer-reviewed academic journals Excellent interpersonal and
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computer science, mathematics, computer engineering, or relevant technical field First-author peer-reviewed published papers (or under review) Proficient programming experience in Python and libraries (e.g., Pytorch
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include algorithm design, prototype implementation (e.g., in Matlab/Python), deployment (to the agents’ autopilots using the Robot Operating System), and experimental testing (on unmanned vehicle swarms). A
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an asset Extensive coding experience (R, Python) would be a strong asset Supervisory experience with students or research staff Application Instructions Applications must be submitted via Interfolio by 18
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experience in embedded, real-time software in C/C++ and Python with several years of practice Experience in maintaining high-quality code on Github Ability to visualize experimental results Effective inter
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experience in chemostats, culturing of phytoplankton and/or bacteria. Strong bioinformatics skills and a proven ability to perform data analysis using programming environments such as Python or R is also
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programming languages and tools commonly used in robotics, such as C , MATLAB, Python, and ROS. Excellent communication and collaboration skills, with the ability to work effectively in an interdisciplinary
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, programming and coding (preferably using Python and C++) along with GUI and simple computational tool development. The candidates will work in a multidisciplinary environment consisting of PhD-level scientists