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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
programming languages such as Python, FORTRAN, or similar, and an experience with using an FEA software. You must be fluent in spoken and written English. Your education must correspond to a five-year Norwegian
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image analysis, deep learning as well as mathematics. You have substantial expertise in programming, especially in Python and Matlab. You are independent, meticulous and work efficiently. Since
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an allied field. An MSc degree in a relevant area is desirable though not necessary. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning
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Experience with R, Python, image analysis, or genetic data analysis (GWAS) is an advantage Motivation to work in interdisciplinary and international research environments You need to: Be able to work
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-throughput experimentation is desirable. Proficiency in programming languages (Python/MATLAB) commonly used in machine learning applications is desirable but learning can be completed during the PhD. Excellent
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programming language (preferably Python) What we offer: A fully funded 75% doctoral position (TV-L E13) for up to 36 months • A competitive salary based on TV-L E13 for Bavaria • Enrollment as a doctoral
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-theoretic models is a plus; Proficiency in programming (e.g., Python, Matlab, C++); Strong interest in fundamental research on autonomous systems and decision-making. No prior knowledge of contextual radio
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experience in academic writing for publication (e.g. first or co-authored peer reviewed papers) Well-developed statistical software skills (preferably in R, Python, GIS) Competences in quantitative research
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, modelling and simulation in CFD/FEA. Experience with scientific programming (at least Python and C++). Excellent written and spoken English. Very strong team working skills in international, interdisciplinary
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economics, market design, and policy-relevant research. Experience with numerical, computational, or simulation-based methods (e.g. Matlab, Python, R, or similar); experience with statistical software is an