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engineering, structural dynamics, basic isolation, finite element modelling. Proficiency and/or interest in programming languages (e.g. MATLAB, Python, R) and software platforms such as OpenSees, ABAQUS, LS
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to research this topic. Interest in laboratory work and basic technical understanding. Fluent written and spoken English. Programming skills in e.g. Python, R, Matlab and Java. Experience in image processing
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are expected. Knowledge in parallel programming is desirable. Prior knowledge in differential-algebraic equations, Gaussian processes or kernel based methods is a plus; programming experience in Python or C/C
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into account as disruptive events, and which strategies can be derived from this for a resilient system design. Your contribution to scientific analysis: Further develop existing energy system models in Python
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Engineering, or related fields. Solid background in structural dynamics, wind engineering, and finite element modelling. Experience in programming (MATLAB/Python) and/or structural analysis software (e.g
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/or interest in programming languages (e.g. MATLAB, Python, R) and software platforms such as Autodesk, ALLPLAN, ANSYS, REVIT, Tekla, Rhino, Grasshopper, etc. Basic knowledge in the areas of signal
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, Origin, R, Python) Driving licence (class B) is desirable The position will be filled by 1 October 2025 at the earliest. We offer an exciting position with varied activities in an interdisciplinary working
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qualifications A masters degree in neuroscience, physics, evolution and ecology, or related quantitative fields. Prior experience with Python or Matlab, and a willingness to learn analysis tools. Excited about
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-oriented way of working A distinct advantage would be: Experience with data analysis and scientific programming (e.g. Origin, Igor Pro, Python, Matlab, Mathematica) A good command of written and spoken
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science and energy technologies Basic knowledge of artificial intelligence and data analysis methods Programming skills, ideally in Python Independent and analytical way of working Reliable and thorough