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Field
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computational platforms (e.g., COMSOL) to simulate complex membrane systems. Strong publication record in membrane mechanics. Knowledge, Skills, and Abilities Excellent analytical and mathematical skills
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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conjecture, -- Langlands program and related problems, -- algebraic geometry and complex geometry, -- partial differential equations and in particular Navier-Stokes equations, -- stochastic analysis
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initiation, focusing on complex cell¿cell communication dynamics (e.g., Nature 2024 PMID 39112713) The successful candidate will work closely alongside a postdoctoral research associate, Dr Iannish Sadien
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A postdoctoral position is available for highly motivated individuals with a background in Computational Neuroscience to analyze complex time-series data from ongoing 2-photon microscopy experiments
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hydrodynamic stability analysis that has applications in the study of the development of singularities, the long-time behavior of complex systems, the formation of spatial patterns, the transition between
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, Mathematical Statistics, Signal Analysis, Dynamic Systems, or a comparable field. The ideal candidate has strong skills in building and implementing statistical models. The applicant should have documented
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Python, for processing and interpreting complex proteomics data Familiarity with proteomics software for data analysis, visualization, and management Experience with biological samples (e.g., FFPE, plasma
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satisfactory performance and funding availability. Applicants should possess a Ph.D. degree in epidemiology, biostatistics, applied mathematics, data science or other related disciplines. They should have strong
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optimization algorithms for both estimation and inference. Special emphasis is placed on advancing statistical approaches to address the demands of increasingly complex, high-dimensional, and unconventional data