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) statistics and data science Experience with mathematical modelling (e.g., linear algebra, linear models methodologies) Demonstrated programming skills in C/C++, Python, R or comparable languages. Experience
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observations and field data collection. Strong competence in data preparation/analysis/visualization with Python, R, or a similar scripting and visualization language. Experience with high performance computing
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scientific discipline or equivalent experience is required. Additional Qualifications ● Should have a demonstrated proficiency in R and/or Python, or related languages. ● Experience with and solid understanding
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DAS datasets, including signal processing, quality control, feature extraction, system identification, imaging, inversion, or source characterization, is highly desirable. Proficiency in Python, C
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++, Python. Experience with Machine learning technologies (e.g., LLM, and Machine Learning) is an advantage Experience with Hardware, e.g., FPGA acceleration, is an advantage. Excellent communication skills
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signal processing, imaging, inversion, or source characterization, is highly desirable. Proficiency in Python, C++, or Julia, with strong numerical modeling, data analysis, and reproducible code
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Python, C++, or Julia, with strong signal processing, numerical modeling, and data analysis skills; experience with Git-based workflows and AI/ML for science is a plus. Hands-on experience in structural
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and library preparation is required. The applicant must have strong independent bioinformatics and programming skills in R, Python or equivalent. The applicant must be able to perform end-to-end
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programming, distributionally robust optimization, optimal transport, or reinforcement learning is highly desirable; • Programming skills in Python are desirable, especially experience with numerical
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Publication track record is an advantage Proficiency in basics of programming languages such as ROS and Python Excellent communication skills, written and verbal – able to communicate complex concepts clearly