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Two year postdoc position at Aarhus University for single molecule FRET based investigations of l...
A postdoc position is available for an initial period of two years with possible extension to three years at the earliest start of 1.9.2025, but a later starting time can be negotiated. The project
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and experience with diagnosing ROMS simulations and observational data, running ocean models, coding, time series analysis, and internal wave processes. The successful candidate will be required to pass
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for comprehensive systems biology modeling. Identification of causal relationships and biomarker discovery through integrative approaches. Time-series and longitudinal multi-omics data analysis for disease
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transport modeling and machine protection strategies for the EIC accelerator complex. This position will focus on Monte Carlo simulations to characterize the radiation environment resulting from beam losses
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 19-May-25 Location: Upton, NY, Type: Full-time Categories: Academic/Faculty Internal Number: JR101837 Brookhaven
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Postdoctoral Research Associate in Plant Molecular Biology ( Job Number: 25000664) Department of Biosciences Grade 7: - £38,249 - £45,413 per annum Fixed Term - Full Time Contract Duration: 3 Years
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modelling of shapes changing in time, and involves mathematical and statistical techniques from statistical shape analysis, Riemannian geometry, time series and stochastic processes, and Bayesian statistics
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and other machine learning models (especially neural network models, time-series models) and coding in python and R. Strong collaborative skills and ability to work well in a complex, multidisciplinary
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the more extreme climates of the future and (2) redesigning root development via fine-scale perturbations of hormone dynamics with minimal off-target effects. The Jones group at SLCU has recently engineered
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approximations for thermodynamically consistent PDEs, reduced order modeling of high dimensional data, feature extraction in medical data, discovery of dynamical systems from time series data, machine-learning