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. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based estimation from images and text metadata. Build and evaluate monocular depth pipelines and
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(AWS, Azure/GCP) Experience in open source software development. Knowledge of GPU-based computing, including multi-gpu/multi-node parallelization techniques will be valued. Fluency in spoken and written
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in high-performance computing using MPI. Experience in GPU programming using OpenACC, CUDA, CUDA-Fortran, Julia, or related tools. Experience in CFD meshing software. TU Delft (Delft University
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environment, which brings together more than 400 researchers across disciplines. The collaboration provides access to substantial computational resources (GPU nodes), advanced high-throughput instruments
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2 Oct 2025 Job Information Organisation/Company University of Innsbruck Department Digital Science Center Research Field Technology Computer science Neurosciences Engineering Biological sciences
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leading-edge scientific challenges and needs. The NanoSIMS lab is specialized for studies of presolar grains and ancient planetary materials. ASIAA has a dedicated CPU cluster, several GPU servers, as
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challenges and needs. The NanoSIMS lab is specialized for studies of presolar grains and ancient planetary materials. ASIAA has a dedicated CPU cluster, several GPU servers, as well as access to the National
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» Computational chemistry Computer science » Modelling tools Computer science » Programming Computer science » 3 D modelling Researcher Profile Established Researcher (R3) Positions Other Positions Country Poland
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and optimize large-scale training and inference runs for foundation models on JUPITER (multi-GPU/node, mixed precision, parallelization, I/O optimization) Integrate multimodal data sources (e.g., scRNA
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signal processing and/or survey datasets. ML & AI techniques and applications. HPC and orchestration of scientific data processing workflows. Parallel computing (GPU & CPU). good software engineering