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Field
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experience with probabilistic or computational modelling. Experience with language model evaluation, cognitive modelling, reinforcement learning, goal-directed behaviour, learning theory or large-scale GPU
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learning (ML) techniques, Internet of Things (IoT), and software and cyber security engineering. The group comprises leading experts in software engineering, cyber security, machine learning and cloud/edge
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with neuroimaging and neural signal processing tools, including fMRI, structural MRI, diffusion MRI, EEG, or related modalities. Strong publication record in AI, machine learning, computational
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processing. Experience with applied statistics, signal processing, and machine learning. Preferred Qualifications: Master’s Degree (foreign equivalent or higher) in Data Science with graduate-level coursework
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-trivial software. Solid understanding of algorithms, numerical methods, scientific computing, or machine learning. Ability and motivation to write clean, maintainable and well-tested code. Strong interest
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Linux/Unix, and running algorithms on CPUs and GPUs Deep learning frameworks, particularly Python and PyTorch Medical image segmentation and computer vision model development Large language models and
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their understanding and utilization of HPC resources. KNOWLEDGE Required Knowledge of a variety of HPC systems (CPU, GPU, storage systems, file systems, networking, virtualization, job schedulers (Slurm) and scientific
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
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the option to partly work from home We live by the principle of Lifelong Learning . Benefits such as public transport subscriptions and car sharing, access to the wide range of ASVZ sports activities
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imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP, BLIP), fine-tuning large language models for clinical NLP, and self