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analysis workflows (Python and/or Julia-based; HPC-oriented handling of large datasets). Depending on competence: contributing to research software development supporting simulations and/or data workflows
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to the Arctic Ocean is a requirement. Experience with statistical data analyses including knowledge of R and/or python is required. Experience in biodiversity and/or ecological studies. Proficient in both written
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modelling, including experience in using a programming language suitable for geospatial data analysis (e.g., R, Python). Experience in applying remote sensing methods, for instance in ecological, geophysical
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(R, Python, MATLAB, or similar). Willingness to conduct fieldwork in Arctic conditions. Proficient in both written and oral English. You will be offered the opportunity to participate in digital
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. Strong research background in biometric security, presentation attack detection, or digital identity systems. Demonstrated programming skills in Python, MATLAB, or C++, and experience with deep learning
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is also placed on your: background in structural and fluid dynamics of civil and marine structures experience in finite element analysis and ability to code in Python or similar software motivation and
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using Python and Fortran Experience using non-linear finite element software, e.g., Abaqus Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work
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data • Solid knowledge of R, python and/or similar programming languages • Experience with teaching scientific methods • Experience with interdisciplinary science • Interaction with nature
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Experience with force measurement techniques (e.g., AFM, micropipette aspiration) or image-based stress inference methods Solid programming skills (e.g. Python, MATLAB, C+) Exposure to active matter concepts
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of computational micromechanics. Knowledge of aluminium alloys. Experience using non-linear finite element software, e.g., Abaqus. Experience with programming using Python and Fortran. Experience with conducting