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following computing skills will be considered an advantage: Natural Language Processing and LLMs; R; Python. Applicants must be fluent in English. Applicants who have completed their education outside
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. Documented experience with Bayesian spatiotemporal modelling, including experience with the INLA framework for Bayesian inference Documented experience with programming in either Python or R. Foreign completed
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, freely-behaving recording procedures, signal processing and data analyses Experience with rodent development, colonies maintenance Fluent oral and written English communication skills Python, Matlab, R
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relevant for the research program outlined above with a strong background in nuclear structure physics of exotic nuclei Expertise in scientific computing, including proficiency in C++ or Python programming
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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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analysis). Skills in programming and scripting languages (Python/R/Matlab). A strong command of oral and written English. It is an advantage if you have Experience with cloud-based compute infrastructure
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(IRT) models in small samples. The ideal candidate has prior knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant
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the closing date. Only applicants with an approved doctoral thesis and public defense are eligible for appointment. Applicants must document knowledge of a programming language for data analysis such as Python
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structure physics of exotic nuclei Expertise in scientific computing, including proficiency in C++ or Python programming Fluent oral and written communication skills in English Desired qualifications: It will
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techniques for screening plasmid libraries, such as phage display and yeast display is an advantage. Coding abilities in R/Python are an advantage. The candidate will work in a very ambitious interdisciplinary