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Field
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Qualifications: A PhD in Physics, Astrophysics, Data Science or a closely related discipline is required. Strong proficiency with programming, especially in python and optionally in C/C++. Strict adherence to open
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is required. Strong proficiency with programming, especially in python and optionally in C/C++. Strict adherence to open source software and open science models. Ability and willingness to work as part
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on MATLAB, Python, Julia, Javascript, DigiSilent Power Factory. Possesses a strong academic record, with relevant experience in either industry or academia, and can show experience from paper publishing. Has
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microscopy experiments (e.g. confocal and light sheet) would be advantageous Experience and interest in performing programmatic bioimage analysis (ideally Python) and/or bioinformatics is required Candidate
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for appointment. Good knowledge of programming and data analysis using Fortran, Matlab, Python, or similar programming languages. Experience in using and developing models to simulate natural systems. Knowledge
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Python and R for use in data science and modeling. Excellent writing and communication skills; demonstrated publication record. Preferred Qualifications Knowledge of cancer biology, clinical oncology
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cycle processes, dynamics of oxygen and nutrient cycles, is required. Expertise in scientific scripting, programming, and data analysis (e.g., Python, Matlab, R) is required. Knowledge of climate
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. Analyze large-scale data and summarize findings using statistical software (SAS, Python, R, and/or similar programs). Collaborate with team to improve standardization, utility, and public health potential
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Learning-related field. Programming experience in Matlab, Python, C++ or other relevant language and experience in deep neural networks. Experience and demonstratable knowledge in deep learning, transformer
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Science, Engineering or other Machine Learning-related field. Programming experience in MATLAB, Python, C++ or other relevant language and experience in deep neural networks. Experience and demonstratable knowledge in