80 polymer-material-simulation-research PhD positions at Technical University of Denmark in Denmark
Sort by
Refine Your Search
-
research, educational activities, and innovation at a high international level within e.g. energy, catalysis, and materials. The overall aim is to contribute with new knowledge about basic scientific
-
Rodrigues, Prof. Kira Vrist Rønn (SDU), and Associate Prof. Line Harder Clemmensen (KU). You will work on research focused on developing AI-enhanced Agent-based Simulation tools to support Intelligence
-
years). Evidence of English proficiency. Copy of Bachelor’s and Master’s certificates. Copy of Bachelor’s and Master’s transcripts. Any additional material useful for the assessment of the candidate (e.g
-
Job Description DTU Electro seeks a highly motivated PhD candidate to join the Perception and Cognition for Autonomous Systems Group (PCAS) in an alliance/strategic partner research project funded
-
, materials technology, manufacturing engineering, engineering design and thermal energy systems. Technology for people DTU develops technology for people. With our international elite research and study
-
scheme lies in the core of the PhD project leveraging limited sensing information from key locations on the offshore infrastructure. In this regard, your research will focus on the assessment
-
competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
-
market frameworks and business models for fair value distribution will be analysed. Responsibilities and qualifications Your primary research tasks will include: Develop and simulate coordinated control
-
caused by the exposure to diverse simulated weather scenarios and urban traffic loadings. Responsibilities Your responsibility is twofold. First, it is driving and performing the research efforts as
-
renewable energy sources into the power grid. Key research questions may include: How can machine learning be leveraged to improve the accuracy and speed of dynamic simulations in renewable power systems