72 gis-python "NTNU Norwegian University of Science and Technology" PhD research jobs
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter
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proficiency in: Linux. git, remote computing, containerization technology. Ideally, candidates should have experience with Python, and knowledge in modern Data Science methods. Knowledge of neuroimaging
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and statistical data analysis Excellent written and spoken English skills Experience with TMS and proficiency in relevant software (e.g., MATLAB, R, Python, or SPSS) is an advantage Key responsibilities
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faculty members, PIs. • Python, Java, or similar language experience. • Familiar with popular machine learning packages, e.g., Pytorch. Special Instructions to Applicants: In order to be considered, you
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environment Programmers comfortable processing and analyzing large data sets in Python; experience with C++ (or another low-level language) is a plus Strategic thinkers with demonstrated interests in strategic
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programming skills in Python and/or C++ Practical knowledge of ML libraries and frameworks, such as PyTorch or TensorFlow, especially in production environments Hands-on experience applying deep learning
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and phylogenetic comparative analyses using R or Python Present research findings at scientific meetings and symposia Prepare and contribute to the publication of results in peer-reviewed journals Your
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but not mandatory). A strong interest in materials simulation, quantum materials, and condensed matter physics. Experience with Python. Fluency in English is required. Finnish language is not required
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into longitudinal studies and in using functional MRI and scripting-based languages (e.g. Matlab, R, Python). They must also have experience of conducting research with human participants and of performing highly
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analyse large, multidimensional 4D STEM datasets. Develop or adapt software tools (e.g. Python, MATLAB) for image reconstruction, phase mapping, and quantitative analysis of ferroelectric domain wall