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public health. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in
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/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with
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-Geometric Foundations of Deep Learning or Computer Vision KTH Royal Institute of Technology, School of Engineering Sciences Job description The Department of Mathematics at KTH welcomes applications for a
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. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or
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related to staff position within a Research Infrastructure? No Offer Description Description of the workplace Automatic Control is an exciting and broad subject, covering both deep mathematics and hands
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risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include
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decision-making in multi-agent autonomous systems by leveraging and combining deep-learning based motion predictions and optimization-based motion planning. Key research questions include multi-modal
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combining deep-learning based motion predictions and optimization-based motion planning. Key research questions include multi-modal uncertainty awareness and generalization and transferability across
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of statistical analysis is required. Strong programming skills in Python, R and Linux-based software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing