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Field
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papers. An ability to analyse data in R, MATLAB or Python is essential. The candidate will join an international, multi-disciplinary team with cross site visits throughout the project. A PhD student also
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publications Excellent command of English Strong proficiency in programming languages such as Python, MATLAB, or R Demonstrated methodological expertise in advanced EEG and/or fMRI methods, evidenced by
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in nanomagnetism and spintronics with emphasis on magnetic skyrmions - Expertise in electrical and MOKE measurements - Expertise in micromagnetic simulations - Experience with Python - Micro/nano
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mathematical foundation and programming skills in languages such as Python, Java, Julia, MATLAB, R, and/or C++. Expertise in one or more of the following: machine learning, deep learning, HPC, Docker/Singularity
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) Proficiency in relevant analytical tools and programming languages (e.g., R, Python) Prior experience of writing and publishing papers on a relevant topic The candidate will join an interdisciplinary team
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(XR) wearable devices, for vehicles on-the-move. Programming in relevant language (e.g. Python) and use of/integration with relevant libraries including APIs to external SDKs/modules Write papers
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Computer Science, AI, Machine Learning, Computer Engineer or a related field. Strong background in machine learning and machine learning frameworks Proficiency in Python and deep learning frameworks A combination of
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development and data analysis will be required. A solid knowledge of Python and/or C++ is also essential for this position. Finally, a good command of spoken and written English is required. Additional comments
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software as indicated in application materials. Experience with data analysis in R and/or Python as indicated in application materials. Experience working with AEM surveys of unconsolidated aquifers as
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datasets, and strong programming in Python and C. Familiarity with galaxy-redshift survey or 21-cm data analysis is a plus. The successful candidate will join the cosmology and astroparticle team at LAPTh