Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
modeling of complex information systems, and SDU and the Royal Danish Defence College’s established intelligence studies and practice. By introducing computational modeling to traditional intelligence
-
research in the field of modern high voltage polymer electrolytic capacitors, develop models for lifetime prediction, methods to predict and test for reliability, understand physics of failures at elevated
-
models to detect food safety compliance risks Integrate regulatory, environmental, and microbial data from food SMEs Design user-friendly decision support systems for inspectors and producers Co-create and
-
, Sociology or related fields). The data collection is to take place in Denmark, so the PhD student must be fluent in Danish. Experience with qualitative methods is required, and related research experience
-
industrial processes. Your research will drive a paradigm shift in how TES systems are modelled, integrated, and controlled within industrial settings. You will develop novel, adaptive, physics-informed models
-
are expected to be significant in: Earth system science – by improving models of Earth surface evolution and enabling better predictions of landscape response to climate change. Engineering and applied physics
-
Job Description The project takes place in the Quantum Light Sources group at DTU Electro, where we design, model, fabricate and test sources of single photons or entangled photon pairs
-
. Work will also involve electrochemical modelling using existing models and using AI based tools. The focus of the work will be to cater to the needs to high voltage/power in power electronic systems
-
and what they mean for the system’s efficiency and safety. You will develop models of AI bidding strategies, analyze strategic interactions using game theory, and design optimization methods to identify
-
aspects of cheminformatics. The position is founded by the Challenge Programme of the Novo Nordisk Foundation: “Mathematical Modelling for Microbial Community Induced Metabolic Diseases ”, led by Prof