26 image-processing-and-machine-learning Postdoctoral positions at University of Luxembourg
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities and Machine Learning/AI on organisations from both the private and public sectors
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deep reinforcement learning (DRL) methodologies for both gene-level and edge-level perturbation control, represents a significant advancement in the computational toolkit for cellular reprogramming
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be found on hpc.uni.lu . The activities include classical HPC applications, such as simulation and modeling, as well as artificial intelligence and machine learning, which bridge computational science
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be found on hpc.uni.lu . The activities include classical HPC applications, such as simulation and modeling, as well as artificial intelligence and machine learning, which bridge computational science
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of neuronal, astroglial and microglial markers in zebrafish and mouse brain samples In vivo imaging of specific brain cells in the transgenic zebrafish and mouse lines Develop phenotypic rescue assays, based
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algorithms, or demonstrated ability and willingness to learn quickly Fluent English Ability to work both independently and in collaboration We offer Multilingual and international character. Modern institution
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We seek a highly motivated bioinformatician or computational biologist who is well versed in the statistical and machine learning analysis of biomedical data and bioscientific programming for a
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” approach. By harmonising and analysing diverse biomedical data, while focusing on the secure data processing and predictive modelling, we aim to drive progress in translational medicine, improving
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phenomenology; experientially taking a belief to be true when we first acquire it and if we later entertain it in thought. On our view, if a belief doesn’t seem true, it won’t be able to play a role in our
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. The objectives are to systematically document and analyse organisational and pedagogical processes related to the implementation and consolidation of institutional quality management systems in non-formal