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Information Systems engineering. The group conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, and Machine Learning/AI 5G on organisations from
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-driven approaches to health, society, and policy. BISI combines expertise in epidemiology, biostatistics, health economics, and machine learning to tackle complex societal challenges. BISI is actively
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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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expertise in architectural design and methodology. You can demonstrate experience in practice in developing and/or customizing digital workflows and tools. Experience with automation and machine learning
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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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in an academic environment for a 4 years period in view of a PhD degree. • You have a strong background in wireless and mobile networks and machine learning • You have excellent coding skills; hands
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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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regulatory network reconstruction and wide range of machine learning approaches The host labs will provide financial support for the whole length of the PhD. The applicant will be expected to seek independent
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relations and in interaction with organizations, to gain insight into computer-mediated communication, its applications and effects, and to pursue evidence-based communication strategies, instruments and
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biopharmaceuticals. The research at CMB pushes the boundaries of biomolecular and bioinformatics research and engineering technologies. VIB.AI studies fundamental problems in biology by combining machine learning with