Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
: Odense, 5230, Denmark [map ] Subject Areas: mathematical theory, algorithm development, error correction, adaptation of GBS-based algorithms to other quantum computing platforms, and the development
-
. The candidate will design algorithms that synchronize and fuse heterogeneous data sources to improve perception robustness under variable underwater conditions. Segmentation and classification: Training
-
Localization and Mapping) algorithms to enable reliable navigation of the UUV relative to the USV and the environment. This includes handling the challenging conditions of subsea localization (limited GPS, murky
-
demonstrate the ability to use this background to: Develop innovative decision-making strategies that seamlessly integrate operational research techniques and machine learning algorithms. Engineer solution
-
achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
-
thus including sensing systems, tool condition features selection, algorithms for automated signal preprocessing, feature extraction and decision making based on ML and AI. An integral part of
-
properties of skeletal muscle during static and dynamic contractions. The student will also participate in early-stage algorithmic work to model muscle architecture and behavior across contraction types. In
-
achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
-
thus including sensing systems, tool condition features selection, algorithms for automated signal preprocessing, feature extraction and decision making based on ML and AI. An integral part of
-
for behavioural and security properties; efficient algorithms for model checking, learning and synthesis; improved explainability and safety of machine learning models, e.g. by integrating neural and symbolic