Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Technical University of Denmark
- University of Southern Denmark
- Nature Careers
- University of Copenhagen
- Aalborg University
- Technical University Of Denmark
- Aalborg Universitet
- Aarhus University
- ; Technical University of Denmark
- Copenhagen Business School , CBS
- Max Planck Society
- Nanyang Technological University
- Roskilde University
- Technical University of Denmark;
- 4 more »
- « less
-
Field
-
conversion reactions. The second position is focused on modelling stability of electrocatalyst materials. The aim is to develop a framework to predict metastability of catalyst materials. Among the methods
-
mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
-
a team of 25 colleagues dealing with CCUS, and industrial partners in Denmark as well as abroad. Your primary tasks will be to: Apply AI in context of capture solvent modelling Understand and analyse
-
proficiency in relevant programming languages (e.g., Python, C++) and tools such as ROS. Experience in simulation and digital twins, as well as the use of synthetic data for training machine learning models, is
-
PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
planning and scheduling. In the furthermore, core works are envisioned in (c) initiating novel VLE design using foundational Digital Twin for Construction Safey (DTCS) components (e.g., city models, nD BIM
-
, supervise, and assess written and oral performances. International applicants are expected to learn Danish. Aalborg University works with a problem-based learning model(PBL). Documented experience with PBL is
-
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, while avoiding humidity and gas exposure
-
both technical insight into data modeling and a solid understanding of how real-world engineering data is generated, structured, and used. We are seeking motivated candidates with strong programming
-
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
-
applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at https