28 complex-network-"https:"-"Integreat--Norwegian-Centre-for-Knowledge-driven-Machine-Learning" positions in Denmark
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
challenge: how to evolve classical communication networks to support both traditional data and the unique requirements of quantum information systems (https://www.classique.aau.dk). CLASSIQUE will address a
-
, and capacity building across the European reference laboratory network. The role of our new colleague will encompass combining knowledge on advanced laboratory expertise with coordination, quality
-
infrastructures across buildings, industry, district heating, electric power grids, and sector-coupled energy networks. Through interdisciplinary research, large-scale collaborations, and active participation in
-
together a large network of European stakeholders across universities, industry, training providers, and policy organisations, with the goal of strengthening Europe’s quantum talent pipeline and accelerating
-
the ability to perform complex data analyses. Has experience with implementing computer-based experiments as well as field experiments. Has professional proficiency in English, both written and spoken
-
distribution infrastructures, and heterogeneous networks and storage environments based on Cloud based services, CDNs and Edge Computing. This complex chain of delivery has negative implications
-
history, list of publications (applicants applying for the position as associate professor should indicate scientific highlights), H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including
-
research activities, and you like to explore research collaborations with colleagues inside and outside your network. You must be responsible for the teaching of courses. DTU employs two working languages
-
problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control
-
safer, more reliable, and more sustainable renewable energy systems. You are driven by scientific curiosity, enjoy working with complex multi-physics models, and are eager to advance probabilistic methods