Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame factorization methods
-
international research environment covering a wide variety of research areas, such as algorithms and data structures, machine learning, computer graphics and vision, database systems, artificial intelligence
-
pruning), carbon-aware computing, minimizing algorithmic complexity, maintenance requirements, mapping energy efficiency and related aspects using KPI (key performance indicators) with respect to ESG
-
constraints, focusing on long-term reliability and autonomy. Robust operation and control of decentralized PV-battery systems: Explore control algorithms and operational approaches that maintain stable
-
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
-
and participation in international conferences. Since you will be part of the research center, which consists of researchers with different backgrounds, you are expected to participate and contribute
-
AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
Thermography. This raw dataset is needed to be processed and annotated to train supervised and unsupervised AI models. The research will aim to develop deep learning algorithms for damage classification
-
order to be successful, you bring: MSC in Computer Science, Physics, Engineering, mathematics or related disciplines with a strong background in data analysis, mathematical modeling and algorithms Good
-
-term ambient monitoring will capture operational vibration patterns due to anthropogenic and natural excitation. The models will help interpret how different damage scenarios affect the ambient vibration
-
may respond differently to the same exposure over time. Managing these acute and chronic training effects is a foundational task for practitioners, who must monitor players frequently to inform