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, network analysis, or machine learning are a plus Good organisational skills and ability to work both independently and collaboratively Effective communication skills and an interest in contributing to a
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Bayesian computational statistics, differentiable programming, and high-performance computing, the project aims to deliver robust, interpretable, and scalable methods for metabolic flux analysis. You will
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learning models to predict ion-exchange isotherm parametersIntegration of predicted parameters into the CADET chromatography simulation framework Simulation and analysis of batch and gradient elution
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to numerous satellite sites of care across the region with the goal of providing care ‘close to home’ whenever feasible (https://pediatrics.uchicago.edu/). Our mission is to improve the health of every child
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sound understanding of data evaluation Prior experience with single-cell data analysis, network analysis, or machine learning are a plus Good organisational skills and ability to work both independently
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to augment classical spike train analysis methods particularly those developed by Prof. Grün and others for detecting synchronous spiking activity with AI-based enhancements. After profiling the classical
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particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design
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across subsets of neurons to low-dimensional manifolds of high-dimensional space of population neuronal firing rates. Thus neuronal experimental data are to be analyzed for both aspects by PCA analysis and
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on model behavior. We will divide our work into three thrusts: Thrust A: A first major objective will be to augment classical spike train analysis methods particularly those developed by Prof. Grün and
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. Thus neuronal experimental data are to be analyzed for both aspects by PCA analysis and statistical multivariate methods to extract spatio-temporal spike patterns. Finally both results will be linked and