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Field
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expertise in methods such as machine-learning force-fields for spinful materials, or multi-fidelity Bayesian models that can learn machine-learning force-fields along with effective spin Hamiltonians from ab
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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. Experience with Bayesian methods, graph/network analytics, reinforcement learning, or other advanced AI approaches relevant to industrial systems. Experience with geospatial analysis, spatial data integration
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model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian
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, or multitrophic interactions is a strong merit. Experience with computational methods such as multilayer networks, Bayesian inference, or higher-order network models is also a merit. The ability to communicate
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-throughput data in the context of human disease. Strong programming skills are essential, particularly in Python, R, or MATLAB, alongside familiarity with Bayesian methods and statistical modelling. A proven
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assessment models, and statistical modeling in both frequentist and Bayesian frameworks • A solid track record of publications Appointment Type Restricted Salary Information Commensuate with experience Review