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field Proficiency in at least one programming language (Python, R, C++, Julia, …) Good analytical skills with a sound understanding of data evaluation Prior experience with single-cell data analysis
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data sets, which have to be evaluated in order to obtain a holistic understanding of very complex systems. Visit HDS-LEE at: https://www.hds-lee.de/ The position is placed at the Institute for Advanced
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understanding of data evaluation, modeling, and interpretation of complex datasets Ability to work independently as well as collaboratively in an interdisciplinary and international research environment Very good
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results and to make parameter estimation more efficient. The project will apply and evaluate these new methods at different sites and time periods, compare them with established approaches, and finally
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: The utility of all developed methods will be rigorously evaluated using both synthetic and real-world datasets. Synthetic benchmarks will be generated using established generative models capable of producing
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opaque “black boxes,” we will integrate post-hoc explainability tools such as SHAP values (SHapley Additive exPlanations) Thrust C: The utility of all developed methods will be rigorously evaluated using
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that can stand in for slow model simulations. These tools will be used to test how model parameters influence results and to make parameter estimation more efficient. The project will apply and evaluate
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++, …) Experience in neuroscience is an advantage Good analytical skills with a sound understanding of data evaluation. Good organisational skills and ability to work systematically, independently and collaboratively
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, or machine-learning frameworks is an asset Strong analytical skills with a solid understanding of data evaluation, modeling, and interpretation of complex datasets Ability to work independently as
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, computer science, or a related field Proficiency in at least one programming language (Python, C++, …) Experience in neuroscience is an advantage Good analytical skills with a sound understanding of data evaluation