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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
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to our work, meaning that we set up experiments in the lab or online to measure behavioural performance (e.g. response times and accuracy) to tasks involving object recognition and categorisation. Our
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Google Earth Engin, R, Python, and STAN (e.g., deep learning, Bayesian regression models, spatial analyses), and running analyses on a high-performance computing cluster. Demonstrated record of publishing
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), and sustainable chemistry. In particular, the role will involve: (i) designing blockchain architectures and smart contracts for CO2 credit tracking, tokenisation of CO2 derived products; (ii
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at the intersection of blockchain, artificial intelligence (AI), and sustainable chemistry. In particular, the role will involve: (i) designing blockchain architectures and smart contracts for CO2 credit tracking
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) Experience in the use of neuroimaging analysis (fMRI, MRI) to study mechanisms of brain function Previous experience of using Bayesian methods in both model development and fitting. Previous experience and
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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human behavior. Preference will be given to candidates with 1) strong knowledge of Bayesian statistical and computational techniques; (2) prior track record of implementing and applying Bayesian
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on hierarchical Bayesian models that allow us to integrate heterogeneous, but complementary, ecological and environmental data. Depending on the background and interest of the candidate, the work will focus on a
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are seeking a postdoctoral researcher to develop methods for analyzing large scale biodiversity and ecosystem function data. Our approach is based on hierarchical Bayesian models that allow us to integrate