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in chemistry and biology, approaches for extracting relevant information from foundation models, and/or methods for adaptive experimental design such as active learning or Bayesian optimization
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requirements, is provided below and in the links. Project descriptions Project 1: Predictive Bayesian inference and foundation models Employment: University of Oslo, Department of Mathematics PhD programme
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, starting date, and project-specific qualification requirements, is provided below and in the links. Project descriptions Project 1: Predictive Bayesian inference and foundation models Employment: University
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project . Fluent oral and written communication skills in English Background in biomarker analysis and/or compound specific isotope analysis and/or archaeometric dating techniques and Bayesian statistics
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datasets. Statistics and mathematics Strong grounding in multivariate statistics, dimensionality reduction, and latent variable modeling. Experience with temporal or dynamical modeling, Bayesian inference
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anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and simulation Demonstrated Applied AI for Healthcare and
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Machine Learning Seminar Group Advanced Tutorial Lecture Series on Machine Learning Non-Parametric Bayes Tutorial Course (October 9, 16 and 28, 2008) Bayesian statistics in other labs Machine Learning and
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project . Fluent oral and written communication skills in English Background in biomarker analysis and/or compound specific isotope analysis and/or archaeometric dating techniques and Bayesian statistics
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long-term experience in investigating the terrestrial cryosphere and is involved in various high-level research projects and centers. (See for instance http://www.mn.uio.no/geo/english/about/organisation
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into embryonic structures - Molecular biology techniques - Advanced imaging - Single-cell sequencing - Drug treatments and effect analysis Where to apply Website https://pica.cineca.it/ Requirements Additional