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Field
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systems • Strong computational background Preferred Qualifications • Experience with GPU programming, shaders, or advanced rendering techniques • Experience integrating external APIs or live data
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, scalability, and cost requirements. • HPC Management: Design, develop, and optimize resource management tools and Slurm GPU plugins for supercomputing clusters. • Deployment & Automation: Oversee
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experience with LLM post-training, including Supervised Fine-Tuning (SFT), Low-Rank Adaptation (LoRA), or reinforcement learning (RL) post-training. Experience with multi-GPU training and a strong working
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excellent infrastructure for deep learning, speech, and audio research, including Aalto University’s large-scale scientific computing cluster with CPU and GPU nodes, access to CSC’s national computing
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infrastructure for deep learning, speech, and audio research, including Aalto University’s large-scale scientific computing cluster with CPU and GPU nodes, access to CSC’s national computing infrastructure
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partners. You will have access to Utrecht University GPU/HPC infrastructure and large, curated microbiome and microbial genome datasets. Additionally, you will collaborate closely with NOAH partners at
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in hardware–software integration, GPU/FPGA acceleration, or translation of research into prototype systems would be an added advantage. Ability to work independently while contributing effectively in
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Preferred Qualifications: Experience applying AI/ML algorithms to severe weather prediction Proficiency in C++ and experience running simulations on GPU-based systems Familiarity with convection-permitting
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: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling, representational analysis, or
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environment with a wide network of collaborators, generous support for conference travel, and access to national HPC resources (NAISS) and local GPU infrastructure. This Postdoc position is part of the