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infrastructure, and GPU/CPU cluster environments. This role leads and mentors a team of Systems Engineers and Administrators while remaining deeply technical and hands-on, actively designing, deploying, and tuning
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platforms such as llm servers, shared virtual GPUs (VGPUs) used by OPS-G, and the broader utilization of cloud resources. Ensuring the smooth operation, availability, and continuous improvement
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facilities. Core responsibilities include the deployment and maintenance of small-scale HPC and compute nodes, GPU workstations, Linux and Windows servers, research data storage and backup solutions
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these resources through a cloud-native Kubernetes environment integrating large-scale CPU and GPU resources, Ceph object storage, BinderHub, Coffea-Casa, Dask, and ServiceX. This platform supports more than 500
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interface which handles large processing tasks efficiently and uses multiprocessing and GPU acceleration where necessary. At the same time, the software must be lean enough to run not only on powerful
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or similar deep learning frameworks GPU know-how: Familiar with GPU workflows and distributed training setups Data competence: Experience in preprocessing, augmentation, and dataset organization; confident
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) specifically for GPU resource management and model training. More Information Location: Kent Ridge Campus Organization: Asian Institute of Digital Finance Department : Credit Research Initiative Employee
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to clinical data; design interpretable representation spaces for tumor-immune dynamics and therapy response. Technical Expertise (25%) – Engineer LLM and diffusion/flow-matching pipelines; integrate multi-GPU
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, Pandas, SQL, Docker, git, etc. PyTorch skills: experience in training machine learning models with one or more GPUs; ability to work with pre-existing codebases and get a training run going A versatile
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models, LLMs and Transformer architectures Excellent programming skills in PyTorch/JAX and experience working with GPUs and high-performance clusters. Strong mathematical skills with excellent