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robustness, fairness, and accessibility. You will design and run reproducible experiments, measure relevant resource metrics, implement prototypes in Python, and communicate results through publications and
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utilizing Python and web-stack technologies (such as JavaScript), to translate theoretical models into functional, testable software in close collaboration with our core research team. Beyond software
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of machine learning such as robustness, fairness, and accessibility. You will design and run reproducible experiments, measure relevant resource metrics, implement prototypes in Python, and communicate results
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image reconstruction, inverse problems, signal processing, computational imaging, or cryo-EM data analysis is required. Strong programming skills (e.g., Python, C/C++, or similar) and experience with
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: Development and design of numerical simulation software Knowledge of programming languages, e.g., Fortran, C/C++, Julia, or Python Numerical methods for partial differential equations (PDEs) It is desirable
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statistical machine learning models and methods, Bayesian learning, or an area related to those mentioned in Work Assignments is also strongly advantageous. Solid programming skills in Python. Experience with
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models and algorithms in Python, with documented experience in PyTorch. The applicant should be knowledgeable with neural networks and furthermore have a strong drive towards performing fundamental
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image analysis, deep learning as well as mathematics. You have substantial expertise in programming, especially in Python and Matlab. You are independent, meticulous and work efficiently. Since
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of novel 2D materials (e.g., thin-film deposition by PVD and CVD). Proven programming skills (e.g., Python) for instrument control and data analysis. You are a highly motivated and independent researcher