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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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implement an integrated, data-driven digital twin of the national energy system, capable of simulating and analyzing multi-energy infrastructures (electricity, heat, gas, hydrogen) across spatial and temporal
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of the Mississippi embayment), surface water hydrology and modeling, model coupling, and spatial/temporal data analysis. The successful candidate will work with Dr. Brian Waldron and CAESER faculty/staff on projects
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with R, statistical and spatial analysis Excellent communication and teamwork skills Excellent organizational skills Experience with field work in post-disturbance forest conditions (desired) Experience
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the remotely sensed LST to compute the spatial statistics, run the HydroBlocks model over the Contiguous United States, and evaluate model deficiencies and model improvements to improve the modeling of spatial
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behavior. Preferred Qualifications: Experience using quantitative spatial techniques to characterize neuron distribution. Brief Description of Duties: The Postdoctoral Associate will assist the Principal
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Postdoc in Statistics Faculty: Faculty of Social and Behavioural Sciences Department: Social Sciences Hours per week: 36 to 40 Application deadline: 24 August 2025 Apply now The attention
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PostDoc in "Sustaining the keystone: Rethinking Antarctic krill fishery management under climate ...
occurred during the October 2024 CCAMLR meeting: the expiration of Conservation Measure (CM) 51-07, which previously mandated that the maximum annual allowable catch of 620.000 t be spatially distributed
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Postdoc in "Navigating uncertainty: Planning marine protected areas in a changing Southern Ocean"...
statistics and the ability to apply quantitative analysis to ecological data A strong background in programming (preferably in R), including data manipulation, statistical analysis, and spatial modelling and
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, signal processing, and data mining A strong background in programming, statistical analysis, and spatial modelling and mapping Highly motivated to work on the subject and eager to work in an