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into the WISE programme will be a part of the WISE Research School Who we are looking for The following requirements are mandatory: To qualify as a PhD student, you must have a Master's degree (masterexamen) of
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experience in programming and handling large (climatological) datasets. You are interested in the fundamental physical processes in Earth’s atmosphere and climate change; You are a team player. You have
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of these research topics. Your position The successful PhD applicant will develop a PhD research program in the research fields listed above, or a closely related field, in close collaboration with the daily
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documentation is in English. Assessment The following criteria constitute the basis of assessment applied in the selection: Meeting the formal qualification requirements for admission into the PhD programme. The
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an English language-based Master's programme (or an equivalent educational achievement in English) shall provide documentation that they have passed one of the following official tests with the respective minimum score
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degree in Computer Science, Math, Statistics or Engineering programs. Ph.D would be an asset. Knowledge of classification algorithms, vision-language models (VLM) and Hugging Face Transformers is an asset
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Degree Dr Course location Nürnberg In cooperation with Institute for Employment Research (IAB) Teaching language English Languages English Full-time / part-time full-time Programme duration 6
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analytical socio-technical skills, including statistical analyses Proven programming experience using Python, R, or others (We will not consider applications that do not showcase that) Having experience
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and statistical data analysis Excellent written and spoken English skills Experience with TMS and proficiency in relevant software (e.g., MATLAB, R, Python, or SPSS) is an advantage Key responsibilities
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to independently conduct research and to work in interdisciplinary collaborations excellent written and verbal communication skills in the English language We offer: cutting-edge research and training in the field