Sort by
Refine Your Search
-
Program
-
Employer
- University of Oslo
- UiT The Arctic University of Norway
- University of Bergen
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- NORCE Norwegian Research Centre
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- Østfold University College
-
Field
-
technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models and algorithms that integrate general and domain-specific knowledge with data
-
to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
-
information at individual level, with specific attention to open and reproducible research, e.g., in the development of codes and algorithms. We will focus on devising computational solutions that can
-
mathematical and computational engine of Artificial Intelligence (AI), and therefore a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world. Integreat
-
for teaching activities. About SURE-AI SURE-AI is a Norwegian AI centre funded by the Research Council of Norway (2025-2030). The primary objective is to create a new generation of algorithms
-
algorithms, benchmarking, model selection and evaluation workflows is an advantage Language requirement: Good oral and written communication skills in English English requirements for applicants from outside
-
without a master’s degree have until June 30, 2026 to complete the final exam. Desired qualifications: Experience with data simulation, clustering algorithms, benchmarking, model selection and evaluation
-
large language models for measurement challenges (e.g., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant response behavior (such as rapid guessing
-
algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
-
limits and human perceptual tolerances. The work will comprise designing networking and computing architectures that integrate prediction and control algorithms, optimizing data transformations, offloading