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Field
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., 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 of experts
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microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle
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engineering Machine learning or AI methods (e.g. anomaly detection, classification, regression, time-series modelling) Programming skills (e.g. Python, MATLAB or similar) Experience with industrial systems
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-ray scattering, materials science, and/or experimental methods at large-scale research facilities Experience in scientific programming (e.g. Python), data analysis and handling/managing large datasets
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/Qualifications Profile - A solid background in continuum mechanics and computational mechanics is required. Advanced knowledge in scientific programming is also requested (e.g. Python, Matlab, possibly FORTRAN
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, Computational Biophysics, or a closely related field Strong programming skills (e.g., Python, C/C++) Knowledge of machine learning frameworks (e.g., PyTorch, TensorFlow) Very good English language skills, ability
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discipline prior to enrolment. The successful candidate should demonstrate: Strong programming skills (e.g. Python, C++ or similar) Solid foundation in linear algebra, geometry, and optimisation Experience
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will use and develop Python scripts for analysing results and may participate in the development of codes such as the observation simulator and the improvement of the controller. The proposed thesis will
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CFD, thermofluids and machine learning. Experience in Python (or another language), machine learning frameworks, or CFD tools such as OpenFOAM is beneficial but not required. Applicants should hold (or
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biochemical models, data assimilation, spatial analysis and GIS approaches. • Programing skills (e.g. R or Python) for data manipulation and visualisation, and to perform statistical analysis (e.g. mixed models