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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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and written. Solid skills in computer programming (Python / Matlab). Experience with CAD and CAE tools. Knowledge of computational fluid dynamics (CFD). Knowledge of finite element method (FEM
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of fluid/structure dynamics and acoustics. Very good knowledge of English, both spoken and written. Solid skills in computer programming (Python / Matlab). Experience with CAD and CAE tools. Knowledge
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in fluid dynamics, turbulence modeling, CFD, and turbomachinery. Experience with CAD and CFD tools (e.g., Ansys Fluent, CFX, StarCCM+, OpenFOAM). Programming skills (e.g., Python, MATLAB). Knowledge
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competence in Python and Matlab The ability to work independently Good written and oral communication skills in English Contract terms Type of position: Full-time research position Duration: Three months Start
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characterization (SEM, EDX, etc) Aerosol physics Data analysis and programming (e.g., MATLAB, Python, or R) Interdisciplinary teamwork Fieldwork and particle sampling e.g. on/from vehicles Swedish language skills
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include: Developing the model using open-source Python software Planning and conducting experiments Analysing teardown reports and experimental data Validating and improving the model Publishing results in
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skills, preferably Python Knowledge of machine learning Willingness to work on a cross-disciplinary project. *for students with an education earned outside of Sweden, a 4-year Bachelor’s degree is accepted
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qualifications Documented experience with data analysis and programming (e.g., Matlab, Python or R). Experience of risk assessment and/or decision analysis Experience of probabilistic methods such as Monte Carlo