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comprehensive and innovative methods to directly test palaeoecological hypotheses using both fluid dynamics simulations and experiments, which will be applicable across the study of life on Earth. https
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-on experience with biological data ● Strong Python skills and experience with PyTorch or JAX, together with the statistical grounding to reason about the models you use ● A GitHub account with code you have
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, protein purification, and/or machine learning is a clear plus. • Experience in data analysis and coding (preferably Python) • A systematic, independent working style and ability to work in a collaborative
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, mathematics, physics, engineering, computational biology or a related quantitative field ● Hands-on experience with biological data ● Strong Python skills and experience with PyTorch or JAX, together
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the translation of ecological science into decision-making. Experience with quantitative analysis, modelling, simulation, or programming, preferably in R, Python, NetLogo, Julia, or comparable tools
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, or layered materials, as well as Python programming (or similar) is a plus. Most importantly, an applicant should be driven by curiosity and should be motivated to work through a difficult long-term (4 years
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publications in peer-reviewed journals and write a PhD thesis to complete your studies Where to apply Website https://apply.refline.ch/673278/3961/index.html?lang=en&cid=1 Requirements Research
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physics-based and data-driven modeling and data-analysis skills (e.g. Python, MATLAB, or comparable tools) are an advantage High interest in interdisciplinary research Excellent communication skills and
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, such as model evaluation and data pipelines, and their applied forms, including training, fine-tuning, and deployment of models using frameworks such as PyTorch or TensorFlow. Solid proficiency in Python
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(Python, MATLAB, or similar) Motivation to combine computational modeling with experimental validation in a highly collaborative environment We offer A unique environment combining theory, computation, and