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Field
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., evolutionary algorithms/strategies, mixed-integer search, multi-objective methods). Strong Python and scientific-computing skills (data handling, experiment tracking, testing, version control). Practical
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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
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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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to ERC project ATRAPA – Active Travel Backlash Paradox, in particular within the WP3 dedicated to electoral analysis. The main objective is to investigate the political consequences of urban interventions
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learning Deep learning model generalisation techniques Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object
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. Prior exposure to experimental data from photon-counting or time-resolved detectors. Experience with Bayesian methods, uncertainty quantification, or real-time data processing. Familiarity with
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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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movements can be preceded by slow movements lasting from several days to several years. These movements can be detected and tracked by satellites, either using radar or optical sensors. Since 2016, data from
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 3 hours ago
complex data to meet project objectives. As essential staff, the postdoctoral researcher is required to maintain research continuity and meet project milestones, even during periods of departmental