Sort by
Refine Your Search
-
facilities, including GPU, server and specialised research equipment. Ensure the operational readiness, reliability and effective functioning of laboratory and research computing infrastructure, including
-
turbulent flow simulations. 3. Background in numerical methods and scientific computing. 4. Proficiency in Python programming and high-performance computing on modern GPU-based platforms
-
computational modelling of additive manufacturing and develop high-performance GPU-based CFD solvers. Qualifications • With PhD degree • Strong research experience in developing GPU-based
-
. Understanding of local hardware optimization (GPU/VRAM management). Comfortable putting together a simple working interface for a workflow (e.g. Gradio) — no formal UX/UI design background required. Able
-
hardware (e.g., GPUs and/or Non-volatile memory) and data science applications.
-
, AI agent orchestration platforms and GPU-enabled AI infrastructure. Responsibilities: Platform operations and reliability Own day-to-day operations of SEA-LION API Farm, our multi-cloud LLM inference
-
computing environments and GPU computing. Proven experience in weather and climate models development and applications. Experience in machine learning, deep learning, or AI applications for atmospheric
-
for GPU-based AI workloads, with control at the level of transformer blocks and attention layers. • Design and build a high-resolution (microsecond-scale) performance monitoring framework to capture
-
the thermal process from GPU computation to radiative cooling Integrate the algorithms into a larger computational satellite simulation environment Identify new cyber-physical systems research topics related
-
distributed training concepts, GPU-based training, checkpointing, experiment tracking, training stability and compute optimisation. MLOps, infrastructure and deployment: Familiarity with Linux environments