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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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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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=sv The employment When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral studies at each faculty is available at https://liu.se/en
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or related domains, will be seen as a strong advantage for applicants. Proficiency in programming (C/C++, OpenGL, R) and experience in data mining or machine learning will be considered advantageous, wherein
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programming skills are considered an advantage. You have excellent communication skills in English, both spoken and written. Additionally, you should be able to work independently, be goal-oriented, and possess
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expected to actively engage in the teamwork and contribute to this collaboration. The employment When taking up the post, you will be admitted to the program for doctoral studies. More information about the
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contract is signed. Applicants must demonstrate strong mathematical maturity and programming skills, and have an excellent ability to communicate in English, both orally and in writing. Furthermore, you
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to the program for doctoral studies. More information about the doctoral studies at each faculty is available at Doctoral studies at Linköping University The employment has a duration of normally four years’ full
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Department of Electrical Engineering, see: https://liu.se/en/article/open-positions-at-isy . The employment When taking up the post, you will be admitted to the program for doctoral studies. More information