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: Scientific programming in Python or MATLAB Structural mechanics, reliability analysis, or probabilistic modelling Data analytics, SHM/SCADA data interpretation, or digital-twin technologies Wind energy systems
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wind energy systems. In particular, experience in one or more of the following areas will be considered highly beneficial: Scientific programming in Python or MATLAB Structural mechanics, reliability
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of advanced wind turbine components. Experience in one or more of the following areas will be considered highly advantageous: Scientific programming in Python or MATLAB Probabilistic methods, Bayesian inference
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interest and solid foundational knowledge in at least one of the research projects Proven competence in computational design workflows, particularly in Rhino and Grasshopper Knowledge of Python, C#, finite
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research area: Python or C#, robotics, additive manufacturing, finite element analysis, or computational fluid dynamics. Experience in teaching and co-supervising thesis students at university level
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Andres Masegosa (arma@cs.aau.dk), Department of Computer Science. (please see: https://andresmasegosa.github.io/ . The project’s domain PI is Professor Jamal Jokar Arsanjani (jja@plan.aau.dk), Department
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bioinformatics including NGS (Nanopore, Illumina, PacBio) Experience with automation and coding in Python or other programing languages Experience with protein software tools like AlphaFold3, Boltz2, PyMOL
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(https://www.classique.aau.dk). CLASSIQUE will address a suite of fresh research challenges defined by the intersection of digital applications that have real-time requirements and quantum resources
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to evolve classical communication networks to support both traditional data and the unique requirements of quantum information systems (https://www.classique.aau.dk). CLASSIQUE will address a suite of
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and Memorial Sloan-Kettering Cancer Center, NY. Read more about the project here: https://health.medarbejdere.au.dk/en/display/artikel/supercomputer-and-ai-to-strengthen-danish-cancer-treatment-new