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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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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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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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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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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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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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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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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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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