Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
Engineering (IMSE) at The University of Texas at El Paso (UTEP) https://www.utep.edu/engineering/imse/ is a dynamic department within the College of Engineering. The department offers one undergraduate degree
-
at: https://industriesofideas.ai/ . Term-limited: This is a term-limited position for two years, with the possibility of renewal contingent upon satisfactory performance, conduct, continued availability
-
-Track) Department: Medicine | School Biomed Sci - Biomedical Informatics Division of Biostatistics and Population Health (BPH, https://medicine.osu.edu/departments/biomedical-informatics/divisions
-
(e.g., Bayesian inference, deep learning), ideally connected to spatial omics, and experience with frameworks like PyTorch, Keras, Pyro, or TensorFlow Application process: Interested candidates should
-
equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our
-
, including (but not limited to): advanced Bayesian techniques to calibrate and update models In an adaptive setup, where decisions ought to balance active learning with exploitative goals; data-driven model
-
: https://go.unl.edu/aboutus As an EO employer, the University of Nebraska considers qualified applicants for employment without regard to race, color, ethnicity, national origin, sex, pregnancy, sexual
-
Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods
-
contribute to groundbreaking projects in OP-MEG and Bayesian computations. This role offers you the chance to collaborate with leading researchers, mentor students, and shape the future of cognitive
-
varying material properties. The resulting response will be analyzed using techniques such as Monte Carlo simulations. Identifying the variability of the model parameters using Bayesian inference