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specifically, your main tasks will include: Support our ongoing research on mechanistic modelling and Bayesian parameter inference from in vitro neural models. Combine theory, simulation, and data-driven
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are usually October and March annually. NB The University has some scholarships under competition each year. More details can be found - https://www.sheffield.ac.uk/postgraduate/phd/scholarships
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University of Oslo and UiT The Arctic University of Norway | Norway, Michigan | United States | 1 day ago
/61927781301 Application and more information: https://www.jobbnorge.no/en/available-jobs/job/303593/phd-fellowships-in-knowledge-driven-machine-learning-in-norway-8-positions Keywords: Bayesian
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biodiversity, and interactions between environment and society. For more information on ASE please visit https://www.ntu.edu.sg/ase . The Yanyan Cheng research group is seeking a Research Fellow
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biological and medical research questions. Strong expertise in (Bayesian) statistics and data science Experience with mathematical modelling (e.g., linear algebra, linear models methodologies
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data, Bayesian statistics, random object analysis, and optimal transport. Experience with neuroimaging data processing and analysis is a plus. The fellows will work with an interdisciplinary team
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 4 hours ago
Continual Language Learning , ( ICLR ’20), Yuanpeng Li, Liang Zhao, Ken Church, Mohamed Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks
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genetics Human genetics Population genetics Evolutionary genetics Bayesian statistics Machine learning Large-scale genomic data analysis High-performance computing Experience in human population genetics
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/organisation/geohyd and https://www.mn.uio.no/geo/english/research/groups/remotesensing ). We are a growing, lively, friendly and collaborative research environment in cold regions sciences, in
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neural networks, information-theoretic and optimal-transport perspectives on representation and generalisation, probabilistic numerics and Bayesian deep learning, and emerging frameworks for scientific