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resource recovery applications. You will combine numerical modelling in tools such as MATLAB and COMSOL with experimental work in the laboratory, where you set up and operate test rigs, characterize water
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programming, e.g., in C++, Python or Matlab. Who we are The successful candidate will be hosted by the Section on AI & Sound. This section is led by Prof. Jan Østergaard. A dedicated supervisory team composed
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communications, fundamental knowledge on computing architectures, and advanced programming skills (e.g., Python, C++, Matlab/Octave). Experience with simulation, emulation, and modelling tools for signal
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. Expertise with the following engineering tools and programming languages can be an advantage but not limited to: MATLAB, PLECS, PSCAD, others. Excellent English language skills (both oral and written
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can be an advantage but not limited to: MATLAB, PLECS, PSCAD, others. Excellent English language skills (both oral and written). Publications in international journals/conferences will be valued
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skills, Experience in programming in Python or another language, e.g., in C++, Matlab, R, Familiarity with basic concepts of dynamical systems, Knowledge of wind turbine dynamics is a plus, Curiosity to
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Python and/or Matlab. Experience with software-defined communication systems, emulation tools, and machine learning techniques is considered an advantage. Most importantly, you are eager to learn and apply
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knowledge and skills in machine learning - Significant experience in programming, e.g., in C++, Python or Matlab. Experience with quantum simulators, such as NetSquid, is a plus; - Familiarity with the
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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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correction and/or mitigation. Knowledge about networking protocols and distributed algorithms. Experience in programming, e.g., in C++, Python or Matlab. Experience with quantum simulators, such as NetSquid