Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Forschungszentrum Jülich
- Technical University of Denmark
- Cranfield University
- DAAD
- Nature Careers
- University of Southern Denmark
- ;
- ; Technical University of Denmark
- National Research Council Canada
- Technical University of Munich
- ; University of Bristol
- Curtin University
- Harper Adams University
- Linköping University
- NTNU - Norwegian University of Science and Technology
- Technical University Of Denmark
- University of Adelaide
- University of Groningen
- University of Newcastle
- University of Nottingham
- 10 more »
- « less
-
Field
-
The project: The deployment of generative AI—particularly Large Language Models (LLMs) based on transformer architectures—in industrial settings poses several critical challenges. Ensuring reliable
-
energy system model workflows Your Profile: Master’s degree in computer science, data science, natural sciences, economics, engineering, mathematics or a related field of study Huge interest in data
-
, reliability, and environmental resilience. The proliferation of intelligent systems has led to increased energy consumption, raising concerns about sustainability and operational costs. Energy-efficient
-
minimise observer variability, leading to more reliable and accurate predictions of patient outcomes. Furthermore, integrating AI-assisted frailty assessments into surgical risk scores can provide a
-
for the automatic generation of future energy technology scenarios based on LLMs, patent data, and scientific literature Investigating the system-level conditions under which future technologies like fusion and
-
, resilience, and/or reliability assessment and their application to power and energy systems The ability to present complex information effectively to a range of audiences with clarity Experience of working as
-
performed under this internship by an Electrical/Power Engineering PhD student would support AEMO with assessing key non-credible contingency risks in future scenarios and could include: Supporting complex
-
opportunity in composites materials for space application research in the Composites and Advanced Materials Centre and the Centre for Defence Engineering at Cranfield university. The focus of this PhD will be
-
grades in the field of mechanical engineering, material science, physics, computational science or similar, preferably with a specialization in the field of theory and/or simulation Strong understanding