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Field
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statistical / data science languages such as R or Python Experience working with geographic information systems Desirable Characteristics: Experience with building spatially explicit models, including
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) to genome-scale datasets · Extensive programming experience in R and/or Python (ideally both) · Strong writing, communication, and interpersonal skills, including a proven ability to work both independently
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components (low-noise amplifiers (LNAs), power amplifiers (PAs), mixers, oscillators etc) Experience in designing analog and mixed-signal circuits for RF applications. Knowledgeable in python programming, AI
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-end technologies (e.g., Node.js, Python/Django/Flask, REST APIs, SQL/NoSQL databases) Demonstrated knowledge of AI/ML methods, such as supervised learning, NLP, or deep learning, with application
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to work on problem solving, programming, data modeling and analysis, scientific writing, and public presentation Ability to work with specific technologies that are commonly used in the group include Python
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and instrumentation. Familiarity with design and simulation tools like SolidWorks, Autodesk Inventor, MuMax, OOMMFi COMSOL, or similar. Familiarity with the programming languages Python, Matlab
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Experience with high-throughput sequencing omics data analysis Proficiency in programming with Python, R, or C++ Candidates without a master’s degree have until 31 August 2025 to complete the final exam
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open source geospatial packages under OSGeo; Experience in programming with multiple languages (e.g. Java, C/C++, Python) for geospatial information systems, agro-informatic applications, crop modeling
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experience in programming languages such as R, Python, and familiarity with NLP tools and techniques (essential) Proven track record in handling and analyzing large healthcare dataset (essential) Track record
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ability to learn if necessary): One or more scientific programming languages, such as Python (preferred) or R, with a preference for functional style and algorithms experience. One or more deep learning