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and revise existing IMAGE machine‑learning components to optimize efficiency, scalability, and quality of results. Implement conversions of existing non‑LLM components to LLM‑based approaches where
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imaging modalities · Strong proficiency in Python for scientific computing, including experience with machine learning and image processing libraries such as PyTorch, TensorFlow, NumPy, SciPy, or OpenCV
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to: Power and Energy Systems, Static and Electro-mechanical Energy Conversion Communications and Signal Processing Computer Vision, Medical Imaging and Multi-modal Understanding Hardware Architecture for AI
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. Qualifications: PhD in machine learning, with experience in applications in computer vision or medical image analysis. Strong publication record in top venues (e.g., CVPR, MIDL, MICCAI, IPMI, PAMI, TMI, MIA
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inverse modeling methods, machine learning, and artificial intelligence techniques. Appointment Details: Start date: As early as March 1, 2026. Annual Salary: $55,000 Application review: Ongoing until