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identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an
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- experience in programming (e.g. Matlab and/or Python) - experience in fieldwork - proficiency in scientific writing - skills in communicating with people who have different backgrounds Your workplace
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programming, especially in Python. You are independent, meticulous and work efficiently. This position requires good communication skills: You enjoy actively participating in meetings and have a strong ability
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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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techniques, and stochastic optimization. Additional knowledge of machine learning and experience with programming in Python and PyTorch would be considered an advantage. You are experienced in conducting
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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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at estimating and optimizing complexity of algorithms. Experience in telecommunications and experience of programming in Python are desirable. The applicant should furthermore have a strong drive towards solving
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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