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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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methods (e.g., PCA, PLS-DA, clustering, neural networks) to enable automated, polymer-specific classification. Optimize workflows for high-throughput imaging and real-world sample variability, minimizing
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
Integration of phenotypic data with omics analysis Explore machine learning and network analysis methods Profile Essential A PhD in Bioinformatics, Computational Biology, Evolutionary Biology
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with (iPSC) tissue culture, neurodegenerative diseases or neuroscience, FLIM microscopy and organoids is a competitive advantage. You have (basic) bio-informatics skills or are willing to learn. Prior
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computational and machine learning approaches, you will decipher genomic regulatory programs and infer the evolutionary patterns of gene regulatory networks in cortical neurons, study their developmental origin
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. Applications will be accepted until the position is filled. Job profile The successful candidate will train recurrent neural networks to learn the generative structure of existing behavioral, EEG and MEG data
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with (iPSC) tissue culture, neurodegenerative diseases or neuroscience, FLIM microscopy and organoids is a competitive advantage. You have (basic) bio-informatics skills or are willing to learn. Prior
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. Applications will be accepted until the position is filled. Job profile The successful candidate will apply linear control theory to existing artificial neural network models of working memory, task switching