Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nanyang Technological University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- INESC TEC
- KINGS COLLEGE LONDON
- Princeton University
- University of Michigan
- University of Texas at Austin
- Lawrence Berkeley National Laboratory
- Monash University
- National University of Singapore
- Nature Careers
- New York University
- Simons Foundation
- University of California
- University of Helsinki
- University of Maryland, Baltimore
- University of Michigan - Ann Arbor
- University of Minho
- University of Oxford
- 9 more »
- « less
-
Field
-
, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300 H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding
-
person will focus on either using and/or developing Vlasiator. Prior knowledge in at least one of the following areas is required: GPU technologies, high-performance computing, parallelisation algorithms
-
development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D
-
part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300
-
on Phase Field modelling and High-Performance Computing (HPC) for geophysical applications. The Asian School of the Environment (ASE) at Nanyang Technological University is an interdisciplinary school
-
) and reproducible research practices Desirable criteria Experience working with generative models or large language models Experience with large scale GPU-based model training and cloud computing
-
development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D
-
and Machine Learning tools and algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability
-
for improved interpretability and generalization. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively in interdisciplinary and cross
-
algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively with