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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
balance Training and international experience in public–private partnerships Mentoring for career development Access to high-performance computational resources (with GPUs) A collaborative environment
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balance Training and international experience in public–private partnerships Mentoring for career development Access to high-performance computational resources (with GPUs) A collaborative environment
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. In addition, CVI2 provides high-performance GPU computing resources that support the design and training of advanced AI models. The research agenda of CVI2 focuses on cutting-edge topics such as 3D
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and or Python required, experience with wireless testbeds desirable, some familiarity with GPU programming desirable (to support collaboration with NVIDIA) Duke is an Equal Opportunity Employer
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models on GPU-based systems; familiarity with HPC environments is an advantage Interest in interdisciplinary research at the interface of AI and genomics; prior experience with biological data
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RTDS. Experience with software development. Experience with use of GPUs, multi-core CPUs, advanced computing (e.g., QPUs). Excellent written and oral communication skills. Motivated self-starter with
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for embedded and GPU platforms. Collaborate with ARSPECTRA engineers and surgeons to create a complete AR guidance pipeline : tracking, SLAM, gaze, user interface Your profile PhD in machine learning
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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-on experience developing generative models Is highly proficient in PyTorch and/or JAX Has experience training large-scale neural networks on HPC or GPU clusters Has experience with representation learning and
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transformer architectures (e.g., ViT/TimeSformer, CLIP/BLIP or similar) in PyTorch, including scalable training on GPUs and reproducible experimentation. Demonstrated experience building explainable models (e.g