Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
in galaxies: how gas and dust form molecular clouds, how stars are born from these environments, and how stellar evolution and feedback return material back to the interstellar medium. Stars are born
-
analysis, or multi-omics integration, with strong competence in deep learning frameworks (e.g., PyTorch/TensorFlow) and data engineering for reproducible research. Familiarity with cloud/HPC workflows
-
. "Studying the origin of the new discovered class of weak CN stars in the Magellanic Clouds using stellar variability" "How do stars merge? Studying the merger between low and intermediate-mass main-sequence
-
to cloud-based machine learning services, on-device ML is privacy-friendly, of low latency, and can work offline. User data will remain at the mobile device for ML inference. Problems: In order to enable
-
, minimizing energy costs and environmental impact. This offers opportunities to work on practical applications in sustainability for cloud and edge systems. Privacy-Enhancing Resource Management: Example: A
-
, and human-explanation agent. The system may be tested in controlled environments such as simulated enterprise networks, containerized cyber ranges, vulnerable applications, cloud workloads, or IoT-style
-
latency, increase throughput, and enable real-time resource management, preparing them for impactful roles in AI, cloud computing, and large-scale system design. A practical example of this project includes