Sort by
Refine Your Search
-
Category
-
Field
-
Bayesian prediction models with uncertainty quantification for trustworthy personalized treatment decisions in the T-PRESS Evidence Ecosystem Framework”. The primary objective of the T-PRESS consortium is to
-
Bayesian prediction models with uncertainty quantification for trustworthy personalized treatment decisions in the T-PRESS Evidence Ecosystem Framework”. The primary objective of the T-PRESS consortium is to
-
“Bayesian Enhanced Tensor Factorization Embedding Structure (BETTER)”, and this PhD project specifically aims at developing a unified, scalable, and interpretable framework for tensor analysis. Specifically
-
is internationally recognized, with interests spanning a broad range of areas - including statistical machine learning, high-dimensional data and big data, computationally intensive inference
-
description The position is connected to the project “Bayesian Enhanced Tensor Factorization Embedding Structure (BETTER)”, and this PhD project specifically aims at developing a unified, scalable, and
-
in English Candidates without a master’s degree have until 01.09.2026 to complete the final exam. Desired qualifications: Solid foundation in Bayesian statistics, empirical Bayes methods and advanced
-
Applications are invited for a 4-year PhD position in Biostatistics, in a project on causal inference for the effects of vaccines and other pharmaceuticals, at the Oslo Centre for Biostatistics and Epidemiology
-
University of Oslo. Place of work is the Department of Biostatistics (OCBE), Domus Medica, Gaustad UiO campus, Oslo. Job description The position is connected to the project “Bayesian Enhanced Tensor
-
, interdisciplinary research, career development, and high quality research. Training Highlights • Core courses in health economics, causal inference, microeconometrics, health inequalities, medical epidemiology, and
-
courses in health economics, causal inference, microeconometrics, health inequalities, medical epidemiology, and healthcare decision-making. • Electives covering data science, public health, implementation