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shared GPU servers to the Delft AI Cluster and the DelftBlue supercomputer. Your supervisors will be Prof. Dariu Gavrila and Dr. Julian Kooij. Job requirements We are looking for a candidate with: An MSc
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collation, mixed precision, multi-GPU; experiment tracking (W&B or MLflow), configuration management (Hydra), strict seed and artefact reproducibility. Graph machine learning. PyTorch Geometric or DGL
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research software and working with high-performance or GPU computing environments. Experience publishing or contributing to scientific articles or conference papers. Personal qualifications Good
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architectures and embedded platforms (ARM Cortex-M, NPU, FPGA, embedded GPU), e.g., via academic courses and/or project courses Research experience (e.g., through a Master thesis work or research internships) is
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, granularity, semantics, attributes) using QGIS and Python for data exploration and preprocessing - designing an approach for automatic change detection using AI techniques (classification, clustering
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-related discipline. 2) Strong programming ability in optimisation or machine learning (e.g., Python/Matlab/C++; PyTorch/TensorFlow). Experience in signal processing/wireless or SDR/GPU prototyping is a plus
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affects cellular differentiation and progression in the URL/external granular layer (EGL), including its role in cell fate specification, chromatin remodeling, and progenitor maintenance. Where to apply
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the start of the HL-LHC, the ATLAS experiment will use a new inner tracker detector, called ITk, which will have greater granularity than the current detector. Reconstructing tracks from the signals
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computing and GPU infrastructure for the development and evaluation of LLM-, VLM-, and agentic AI solutions Research across the entire automotive software development lifecycle, from requirements and software
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selection criteria Good oral and written presentation skills in Norwegian, or another scandinavian language Experience with the CUDA programming model for general-purpose GPUs Experience with the OpenCL