Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied
-
Posting Details Position Information Position Title Assistant Professor - 12 Month, Biostatistics Department Department of Biostatistics Posting Number F250011 Posting Link https
-
, Mathematical Biology, Biostatistics, Mathematical Epidemiology, Computational Biology, Bioinformatics, P robability Theory, Stochastic Analysis, Mathematical Statistics, Bayesian Statistics are encouraged
-
via https://www.mathjobs.org/jobs/list/26674 . Please provide the following required materials: · A cover letter addressing the following o Required qualifications o Preferred qualifications
-
for seeking internal and external funding Application Procedure To receive consideration, a complete online application must be received by electronic submission via https://www.mathjobs.org/jobs/list/26674
-
. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and compressed sensing
-
factorizations, least-squares problems, descriptive statistics, probability rules, probability distributions, statistical significance, hypothesis testing, estimation, Bayesian paradigm. Benefits:https
-
factorizations, least-squares problems, descriptive statistics, probability rules, probability distributions, statistical significance, hypothesis testing, estimation, Bayesian paradigm. Benefits:https
-
, adversarial attacks, and Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering
-
candidate will show in-depth methodological and applied knowledge in the field of machine learning, especially deep learning, experiences in the area of uncertainty quantification, generative and Bayesian