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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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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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advantage. You have (basic) bio-informatics skills or are willing to learn. Prior experience with FLIM (performing experiments, data analysis, e.g. with FLUTE, Flimfit, SPCImage, GSLab, or napari) and/or next
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chemistry economy. The main objective of this multidisciplinary project is the experimental and computational (through plasma chemistry and reactor modelling) investigation of possible approaches to process
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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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. 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
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of avian cognition in a truly collaborative and global research network. The postholder will play a central role in two closely linked initiatives: (1) contributing to a new ECoBird project, in collaboration
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. Immunity 2020; Roels et al. Nat Immunol 2020). As part of the Human Cell Atlas Thymus Seed Network, we have also been involved in high-resolution profiling of the developing human thymus using single cell