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of the Biomaterials and Tissues of the Future. https://cordis.europa.eu/project/id/101226431 This network has 8 host institutions hiring doctoral candidates: Uppsala University, Universitat Politecnica de Catalunya
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(e.g., Bayesian inference, deep learning), ideally connected to spatial omics, and experience with frameworks like PyTorch, Keras, Pyro, or TensorFlow Application process: Interested candidates should
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California State University, Northridge | Northridge, California | United States | about 1 month ago
Hawaiian or Pacific Islander. For more information about the University, visit: http://www.csun.edu About the College: For more information about the Department of Psychology, see: https://www.csun.edu
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Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods
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the Department of Applied Health Sciences and will support the delivery of the NIHR Research Professorship awarded to Professor Joht Singh Chandan (NIHR306365, https://fundingawards.nihr.ac.uk/award/NIHR306365
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design, computer experiments, sequential analysis, shape-constrained inference, time series, and Bayesian analysis. In applied mathematics, these include information theory, coding theory, control theory
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" section on the Applicant Portal at https://uscjobs.sc.edu. Research Grant or Time-limited positions may be eligible for all, some, or no benefits, based on the grant or project funding. South Carolina
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& Partnerships (NSF-TIP) directorate. More information on the project is available at: https://industriesofideas.ai/ . Term-limited: This is a term-limited position for two years, with the possibility of renewal
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. We are interested in candidates with research interests in causal inference or Bayesian methodology, and we also welcome strong applicants from the broader fields of statistics and machine learning
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during