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Master's thesis -An interest in turbine aerodynamics, secondary flows, and real-engine flow physics -Programming and data-analysis skills (Python preferred) -Curiosity and motivation to work on complex
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skills (Python preferred) Experience with processing and analysing 3D data Excellent written communication skills An interest in interdisciplinary research and enthusiasm for developing novel methods
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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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programming are desirable (MATLAB, python, C++ etc). Any experience or capabilities in engineering design or manufacturing methods would be advantageous. Eligibility and Application Due to funding restrictions
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. Experience in programming (e.g. Python, C++). Strong analytical and simulation skills. Desirable Criteria Prior experience conducting research or contributing to research publications. Funding and Support The
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, Computational Social Science, Natural Language Models, Physics, Mathematics, Computer Science, or related subjects. Proficiency in programming languages relevant to research (e.g., Python, SQL, Julia). Experience
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programming and software skills (Python essential; C++/Fortran beneficial) Interest in machine-learning-based regression and surrogate modelling Motivation to challenge established CFD-driven design workflows
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. Programming experience (e.g. Python, MATLAB, C/C++). Desirable (but not required): Background in control theory, dynamical systems, optimisation, or machine learning. Experience with robotics, ROS, simulation
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, or modelling. Familiarity with computational tools (Matlab, Python, or finite element analysis). Analytical thinking and enthusiasm for interdisciplinary research. Ability to work independently and as part of a
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Networks. Knowledge of and experience in Python, TensorFlow, Keras, or other Machine Learning toolboxes, is essential. Knowledge of and experience in Large Language Models is highly relevant. The successful