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Field
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machine learning models with respect to accuracy and uncertainty quantification. - Developing software to implement the goals stated above (most likely in Python). - Disseminating results
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relevant programming languages (e.g., Python, MATLAB, R) is a requirement. Familiarity with downscaling and bias correction of climate data (e.g., from CMIP/PMIP) is an advantage. Experience with
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experience in python, C++ or other relevant language and experience in deep neural networks Strong mathematical capability, especially for Theme 1, with applicants holding an undergraduate or postgraduate
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language, with a strong preference for Python. Desirable criteria: Additional experience in machine learning, statistical learning theory or related fields Fluency in relevant models, techniques or methods
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of physics/maths and biology is essential. Coding competency (in C, Python, Matlab or similar language) is essential. Applicants should demonstrate a publication record appropriate to their level of experience
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, dynamics, robot modelling and control engineering Experience in mobile manipulators and industrial robots Programming languages/libraries: MATLAB, C++, Python, OpenCV Competent in mechanical design Entry
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data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python), including a sound understanding of FAIR data principles, data management and
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gdsfactory Knowledgeable in MATLAB and Python is necessary Knowledgeable in Ansys Lumerical FDTD, MODE, INTERCONNECT, CML COMPILER, MULTIPHYSICS Prior experience in the above will be a bonus We regret to
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PhD in Computer Science, Engineering or other Machine Learning-related field. • Programming experience in python, C++ or other relevant language and experience in deep neural networks • Strong
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, astrophysics, or a closely related field. Knowledge of Unix/Linux-based operating systems and the Python and/or C++ programming languages is required. Experience with particle/astroparticle physics detectors