Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
- Computer Science
- Economics
- Medical Sciences
- Engineering
- Biology
- Business
- Science
- Humanities
- Materials Science
- Psychology
- Chemistry
- Education
- Mathematics
- Social Sciences
- Arts and Literature
- Linguistics
- Electrical Engineering
- Philosophy
- Earth Sciences
- Environment
- Law
- Sports and Recreation
- 12 more »
- « less
-
Conduct literature watch / scientific monitoring on fundamental aspects of computer science, serious games, and agent-based simulation Contribute to the synergy between conceptual, technological, and
-
based on the research proposal. The faculty has prepared a guide for writing project descriptions. Qualifications and personal qualities: The applicant must hold a master's degree in information
-
schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . We are looking for a Research Fellow in Multi-Agent RL for Autonomous Drone Swarm to develop learning-based
-
colleagues, academic program coordinators, and students. Monitors and responds to inquiries from prospective international students, agents, and stakeholders using Slate, Outlook, WhatsApp, and other multi
-
. Mastery architecting LLM-based agentic systems, including orchestration, tool use, and evaluation. Mastery with Linux, HPC, and cloud environments. FLSA Exempt Full Time/Part Time Full Time Number of Hours
-
Scholarship of Teaching and Learning (SoTL) Laboratory to design, build, and maintain a next-generation learning platform that leverages large language models, AI agents, and cloud technologies to support
-
, and validation of membrane permeabilization systems. Establishing parameters to ensure consistent, high-efficiency intracellular delivery of cryoprotectant agents; troubleshoot as needed. Preparing SOPs
-
learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents
-
beyond traditional QA to design intelligent, agent-based testing capabilities that automatically analyze requirements, understand application code, generate reusable test assets, and continuously improve
-
Directions AI for Scientific Computing Neural Operators and Learning-Based Surrogates LLMs and Scientific Agents Agentic AI for Engineering Design Multimodal Scientific AI Alignment and Verification