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including transformers, self-supervised learning, foundation models, autoencoders or related architectures; strong programming skills in Python and experience with a deep-learning framework such as PyTorch
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experience in genetic manipulation of microorganisms Competences and experience in advanced statistics (e.g., multivariate statistics, GLMs) and programming (e.g., python, R) Publication(s) from earlier
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generator code, C++, Python. Excellent verbal and written skills. Presentation skills. Analytical skills. Attention to detail. Salary: Compensation will be commensurate based on the selected candidate's
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relevant programming language (e.g., R or Python). Strong written and oral communication skills. Experience managing multiple projects at once. Willingness to learn new technical skills and gain knowledge in
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scientific programming in Python and the use of frameworks such as PyTorch or TensorFlow. It is also necessary to have experience in federated learning and deep learning applied to medical imaging, as
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experiments Demonstrated ability to analyze experimental data (e.g., in Python, MATLAB, or equivalent) KEY RESPONSIBILITIES AND ACCOUNTABILITIES 1. Design, perform, and analyze nanoscale magnetic resonance
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science related discipline. You will possess established expertise in data analysis including statistical programming (e.g., STATA, RStudio, Python). You will demonstrate previous experience of contributing
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photometry and/or time-series analysis. Research experience in observational astronomy, demonstrated through publications appropriate to your career stage. Solid programming skills (e.g., Python) and
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Classification Title: Post-Doctoral Associate in unsupervised and generative ML for chemistry Classification Minimum Requirements: PhD in Chemistry or related area Strong coding (Python) and
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energy-system modelling, mathematical optimization, control, digital twins, or related areas. Strong programming and numerical modelling skills using Python, MATLAB/Simulink, Julia, hardware in the loop