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histological experiments, measuring and manipulating neural signals using optogenetic and optical tools, and analyzing the resulting datasets using modern computational tools in the Python language. Key
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of scientific programming, e.g. with C++, Python, ROOT or similar. Informal enquiries to Professor Dave Charlton, email: dave.charlton@cern.ch View our staff values and behaviours here Use of AI in applications
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such as R, Stata, Python, or equivalent for data preparation and analysis. You have strong analytical and communication skills, with the ability to contribute to academic outputs such as research papers
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methodologies. Applicants must demonstrate strong programming skills in at least one scripting or programming language (e.g. Python, R, Perl, Nextflow, C++, or Java) and experience in areas such as database
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data analysis on Matlab. • Developing and using models to characterise the soft robots (both sensor and actuator). • Knowledge of programming (C/C++/python/MATLAB), using a prototyping board like Arduino
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computing (e.g. Python, MATLAB, or similar) for data processing and data handling Experience with data processing, analysis and interpretation Excellent interdisciplinary communication – Strong collaborative
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
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strong track record in programming, preferably in Python. The candidate should have demonstrable expertise in computational modelling of solids, preferably using density functional theory methods. Previous
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to scientific problems, preferably with materials or polymer datasets. Proficiency in Python and scientific computing libraries; familiarity with machine-learning frameworks and data processing tools. Experience
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will be advantageous. Knowledge of machine learning or reinforcement learning techniques will be advantageous. Proficiency in algorithm development using Python will be advantageous