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contribute to the excellence of our academic community. We are looking for a postdoctoral researcher with expertise in Bayesian hierarchical spatio-temporal statistics and measurement error methods for a 3
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
and data-driven procedures for pointwise and/or credibility interval estimation of epidemiological indicators, e.g., for the reproduction number R(t) of Covid19. Elaborating on a recent epidemiological
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of autonomous flow reactors for chemical synthesis. The project aims at 1/ developing a new optimization Bayesian algorithm and 2/ improving the process-control software already developed in the team
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/ Postdoctoral Scholar-Paid Direct -Fiscal Year. A reasonable estimate for this position is $69,073-79,881. Percent time: Full-time (100%) Anticipated start: As soon as October 1, 2026 but no later than January 1
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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, such as Bayesian approaches and fossilized birth–death models, to reconstruct robust phylogenies and estimate divergence times. It also investigates macroevolutionary dynamics, including variation in
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Bayes factor hypothesis tests in factorial designs. What are you going to do The envisioned projects will focus on the following activities related to Bayesian inference in factorial designs: Construction
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spectrographs with various spectral resolutions, operating from 0.5 to 28 µm. Our group has developed the Bayesian modeling tool FORMOSA (Petrus et al. 2023). It allows the inference of low-resolution (R = λ/Δλ
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standards. About the research project The postdoctoral project will focus on precision tests of low-energy strong interactions via the ab initio modeling of open-shell, nuclear many-body systems and Bayesian
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on the following activities related to Bayesian inference in factorial designs: Construction and elicitation of informed prior distributions; Critical assessment of default prior distributions; Organizing a many