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such as Machine Learning, Natural Language Processing, AI in Education, Knowledge Representation, and Python-based analytical seminars at the BSc, MSc, and PhD levels. Responsibilities include assisting in
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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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, linear algebra, and statistical modelling Demonstrated proficiency in programming (e.g., Python, PyTorch/JAX, or similar) Strong interest in method development Fluent oral and written communication skills
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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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/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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., 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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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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, computational biology, bioinformatics, data science, or related fields Strong interest in clinical and biomedical data, translational research, and health informatics Experience with programming skills in Python