Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
) development, validation and application testing of a novel distributed fiber-optic acoustic measurement system (DAS) for structural health monitoring (SHM) of underground gas cavern storage facilities and
-
Statistics section, include: Algorithms , focusing on online and approximation algorithms, graph and parameterized algorithms, string algorithms, data structures, combinatorial optimization, algorithmic
-
: Strong mathematical ability. Experience of scientific programming and algorithm development. Knowledge in wireless communication systems and the theory behind. Knowledge in distributed optimization
-
), including algorithm design, implementation, and experimental validation conduct research on distributed processing schemes for hearing aid algorithms, integrating advanced signal processing methods, low
-
PhD level. Work with experts at the Jülich Supercomputing Centre (JSC) to run your algorithms/tools on large distributed computer systems. Write reports and research articles, as well as grant proposals
-
intelligence (AI) and machine learning(ML). Duties This position combines knowledge of the Earth observation (EO) domain (EO instruments, EO data, EO algorithms, modelling, etc.) and AI/ML, as well as data
-
Offer Description Mission: Provide support in the longitudinal acquisition of pediatric polysomnographic (PSG) records and contribute to the development and validation of multichannel algorithms
-
optimization methods for implementation within the framework of the objectives of the doctoral thesis, starting with the exploration of methods based on genetic algorithms. Explore the possibilities
-
The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented
-
causal analysis across distributed datasets while preserving privacy. The successful candidate will be responsible for the end-to-end investigation of novel federated learning strategies for causal