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, Grasshopper, Python). Explore new trajectories for the advancement of AI-supported integrated architecture and its potential impact on the build environment. Contribute to developing open-source tools and code
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skills (mandatory): Strong understanding of sustainable AI or related areas Experience of programming in Python / C / Java or equivalent Experience with using Machine Learning software, e.g. PyTorch
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characterization of photonic devices. Desired qualifications: Theoretical understanding of photonic integrated circuits. Experience with numerical modeling. Experience with Python programming. Additionally, we
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the interdisciplinary lab to bridge environmental concerns and creative design processes. • Investigate the creative process at the intersection of LCA, AI, and early design phases. • Merge Python-based approaches
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at least one scientific programming environment such as Python, MATLAB, or R. Familiarity with structural degradation phenomena—including fatigue, corrosion, and biofouling—would also be beneficial
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Python (or equivalent). Experience with glass science, battery materials and/or atomistic simulations is highly advantageous. All interested candidates are encouraged to apply, regardless of their personal
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scientific programming, e.g. using Python, is expected. As we work in international environment it is important that you have a good communication skills in both spoken and written English, and experience in
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software skills Proven experience with CFD automation, e.g. using PyFluent or similar Python APIs for meshing, solver control, post-processing and data management. Solid experience programming in Python
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optical systems with COMSOL, Lumerical, or similar. Experience with control software and data logging – preferably in python. Good communication skills and a structured approach to work. Why Us? Joining
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DTU Tenure Track Researcher on Nanoreactors for Operando Visualizations of Nanoparticle Catalysis...
experience testing for trace-amounts of contamination/impurities on MEMS structures to iron out false-positives in catalytic experiments. Experience in Python and LabView programming for developing new codes