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Field
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. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state-of-the-art IT infrastructure. Ideal
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(adaptive) imaging strategies and reconstruction. The research combines ultrasound physics, signal processing, machine learning, computational imaging, and clinical translation. Beyond your individual
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of machine learning and clinical oncology, with access to a large multimodal research dataset, substantial GPU resources, and a collaborative scientific environment. Your tasks Design and implement LLM-based
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building and applying state-of-the-art machine learning approaches, including foundation models, variational autoencoders (VAEs), and transformer-based architectures, to integrate single-cell and multi-omic
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fields Knowledge of HPC and very good programming skills in C++ Experience with GPU programming and data-intensive workloads is an advantage Very good command of English Knowledge of German is an advantage
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing using clusters like UPPMAX and GPUs for high-performance computing and parallel
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knowledge of analytics and AI/ML platform services across AWS, Azure, and GCP (e.g., AWS SageMaker/Bedrock, Azure Machine Learning/Azure OpenAI, Google Vertex AI) and how to operate them securely
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cancer—from mechanistic modeling and physical dynamics of metastasis to high-content single cell data and advanced machine learning. Our vision is to integrate patient, model system and high‐dimensional
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: Deep proficiency with modern machine learning, including deep learning, transformers, graph neural networks, generative models, and foundation models and their application across biomedical and chemical