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to accurate sample reconstructions using advanced signal processing and tomographic reconstruction algorithms. With the inclusion of noise the object estimation accuracy will be based on statistical concepts
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; Proven competence on flow measurement techniques and PIV; Familiarity with optics, lasers, image processing and statistical data analysis Familiarity with flow modelling techniques (CFD) or machine
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. An example is the extension to turbulence statistics, rather than just mean velocity profiles. You will join the Process & Energy (P&E) department at TU Delft’s Mechanical Engineering faculty, working in a lab
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mathematical/statistical modelling and programming (e.g. Python, MATLAB, or similar). A research-oriented attitude and strong motivation to deliver ambitious, high-quality work. Ability to work in an
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statistical learning theory and probabilistic models; prior exposure to notions of robustness, resilience, or uncertainty quantification is an advantage. Mathematical maturity and experience with formal
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candidate possesses: A Master’s degree in Environmental Economics, Psychology, Complex Systems, Engineering & Policy Analysis, or a related field; Experience with data analysis and statistical methods; A
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collection. Strong analytic and programming skills (e.g., Python, C#) with proficiency in statistics. Strong skills in qualitative and quantitative analysis. Good communication and writing skills in English
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-year research project aimed at advancing our understanding of foot growth and deformities in children with cerebral palsy. In this project, you will develop statistical shape models and finite element