17 modelling-and-simulation-of-combustion-postdoc Fellowship positions at Hong Kong Polytechnic University
Sort by
Refine Your Search
-
Listed
-
Field
-
) lead market analysis, requirements gathering, and conceptual design; (b) optimise electromagnetic design of motor through simulations; (c) develop control algorithms and models; (d) guide
-
- “Unlocking the value of shared question-and-answer (Q&A): A new business model on paid Q&A platforms”. Qualifications Applicants should have a doctoral degree or an equivalent qualification and must have no
-
) support major BIM platforms, such as Revit and AutoCAD; (c) develop BIM-related functional modules, including model parsing, data interaction, automated modelling, and collaborative design; (d
-
computational modelling data; (c) prepare manuscripts; and (d) perform any other duties as assigned by the project leader or his/her delegates. Qualifications Applicants should have: (a) a doctoral
-
development of Large Language Models (LLMs), Agent AIs, Retrieval Augmented Generations (RAGs) or industrial systems. Applicants for the Research Assistant / Project Assistant post should have an honours degree
-
assist the project leader in the research project – “3D dynamic human head, neck and shoulder modelling for product design”. They will be required to: (a) assist in data analysis and publication; (b
-
twelve months] Duties The appointees will assist the project leader in the research project - “Develop a vision-language model-based smart driving assistant for enhancing safety and convenience
-
experience at the time of application; (b) have experience in InSAR applications for landslides and reclamations, PolSAR/PolInSAR for vegetation mapping, finite element analysis, numerical simulation, machine
-
implementation; b) monitor the project implementation details; c) develop control algorithms and digital twin models; d) guide prototype development and laboratory testing; e) analyse test data and
-
research using methods such as sensing technique, 3D printing, human-computer interaction, simulation, and/or machine learning to address challenges in machinery motion planning and construction safety