Sort by
Refine Your Search
-
with other PhD candidates and postdocs, and opportunities for research to stay with partners, both nationally and abroad, through its international network. This PhD position “Distributed Optimization
-
, e.g. in Python, particularly for machine learning Experience with machine learning theory, evaluation metrics, algorithmic fairness, statistics, or optimization is an advantage Language requirement
-
Experience with machine learning theory, evaluation metrics, algorithmic fairness, statistics, or optimization is an advantage Language requirement: Good oral and written communication skills in English
-
PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
transactions; Ship design and construction; Voyage planning and optimization; Condition monitoring and maintenance; Crew training; Maritime traffic and ship surveillance; Decarbonization and energy management
-
ecosystems where interconnected multi-agents interact strategically in dynamic and uncertain environments. While Artificial Intelligence (AI) optimizes predictions or policies, energy systems are inherently
-
Researcher (R2) Positions Postdoc Positions Application Deadline 31 Aug 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Hours Per Week 37,5 Is the job funded through
-
dynamic and uncertain environments. While Artificial Intelligence (AI) optimizes predictions or policies, energy systems are inherently multi-agent, strategic, and resource-constrained. Each agent has its
-
partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML Enhancement of AI/ML with in-network computing & processing Adaptation & optimization
-
synthesis of timed and probabilistic behavioral models for model checking, performance evaluation, and optimization. The overall objective is to establish formal foundations that bridge static engineering
-
to research on some of the following themes: New algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process