The research project focuses on developing real2sim2real learning and control methodologies for acquiring robust dexterous manipulation skills in complex, content-rich environments.
Work closely with the Principal Investigator (PI), Co-PI, faculty and research team to plan, track, and deliver project milestones. Collaborate with industry partners to support real-world trials and joint research initiatives.
Graph learning engineering: Optimize graph storage, neighbor sampling, feature fetching, caching, CPU–GPU transfer, and training pipelines. Support repeatable evaluation on fixed snapshots and validate sampling or graph-construction changes with researchers.
Train and deploy learned policies on robotic manipulators and mobile manipulation platforms, and troubleshoot performance issues across perception, control, learning, and hardware.
This Research Engineer will need to conduct research on data-driven turbulence modelling. He/ She will also be required to assist on Project Management and administration of research projects.
Support the operation, maintenance, and troubleshooting of laboratory-scale fixed-bed reactor and fluidized-bed reactor systems and associated equipment.