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of the art equipment within DNA and RNA sequencing, laboratory automation, CPU and GPU compute resources, proteomics, metabolomics, and advanced microscopy. This position offers an excellent opportunity
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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background Preferred Qualifications • Experience with GPU programming, shaders, or advanced rendering techniques • Experience integrating external APIs or live data streams • Background in distributed systems
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++ Knowledge of GPU programming. Experience in application performance profiling. Desired qualifications: Experience from working on quantum computing Experience in FPGA development. Contract terms This postdoc
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of spikes by a model Develop proxy apps representing the different processing stages of spiking network simulation code (targeting CPU and accelerators such as GPU or IPU) Systematic benchmarking of proxy
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funded through the EU Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Reykjavik University invites applications
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programming skills in C++ and/or Python; experience with high-performance or real-time computing, e.g. GPU, multi-core, embedded, is desirable Prior experience with SOFA is a clear advantage; experience with
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programming LAMP stack design and implementation experience Knowledge of GPU and FPGA cluster management Experience with federal research compliance and security requirements Background in AI/ML computing
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, Statistics, Biostatistics, Bioinformatics, Computational Biology, or a related quantitative field. Strong programming skills. Experience with machine learning frameworks such as PyTorch or TensorFlow