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analysis; strong letter of recommendation from master project supervisor; Beneficial skill: programming, including some languages of the following list: C, C++, Python, Matlab, Mathematica. Programming
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equations numerically Experience with programming (C, C++, Python, Matlab or alike) Creative pro-active team player with good analytical skills A research-oriented attitude Ability to work in an
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, hydrological modelling, AI-based methods; Experience with programming languages (e.g. Python); Motivation to cooperate in a diverse and multidisciplinary research team; Strong social and communication skills
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skills and programming experience, preferably in R and C++ (or Python, Java or similar); clear interest in the field of biomedical or public health research; good communication skills and excellent command
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) Physics Knowledge of experimental atomic, plasma and/or laser physics is an asset, especially if combined with strong hands-on laboratory skills Programming experience, particularly in Python, is welcomed
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: Ability and motivation to conduct fieldwork in alpine environments, and willingness to obtain a UAV license. Interest in coding (e.g., Python, Matlab, R) and numerical modelling. Enthusiasms for working
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PhD Candidate on The Future of Mixed Methods Research /Junior Lecturer in Methodology and Statistics
are appreciated but not required. Good research skills evidenced by good grades for research methods courses. Excellent data analytical skills, as evidenced by a good command of R and/or Python Experience with
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; experience in either is a plus, not a must. Strong motivation to advance data analysis or modeling is essential. Proficiency in scientific programming (e.g., Python, Fortran), version control, and reproducible
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environmental datasets; proficiency with Python, MATLAB, or similar scientific programming environments. Ability to work with large datasets, develop reproducible workflows, and apply modern data science tools
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programming languages / environments, such as, Python, Rand experience working with High Performance Computing. Basic knowledge of machine-learning concepts (classification, regression, cross-validation