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approaches that address (for example) the intellectual status of the image, the political functions of art, or visual theology across multiple regions. The Mellon Fellow will teach two courses per year: one
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. Proficiency in deploying and managing wildlife camera‑trap networks and processing large image datasets. Experience developing and validating machine‑learning and AI models for image object detection and
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projects as well as general research involving the application of methods from theoretical physics, mathematics, and machine learning with the goal to understand the brain function. Postdoctoral Fellowships
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computer chips. Your role will be to design processes for transforming resources into sustainable materials by modeling reaction pathways from first principles. The project will collaborate with a broad
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foundation models and agentic systems and demonstrated capability to produce workable solutions from theoretical formulations Substantial publication history in top computer vision/machine learning venues
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record of computer programming. Experience in numerical relativity and/or numerical simulation of quantum field-detector interaction Strong written and oral communication skills. Demonstrated ability
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The fellow will be responsible for: Building collaborations with our multidisciplinary team (medical physicists, engineers, computer scientists, nuclear medicine physicians) to develop and implement innovative
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scientists, nuclear medicine physicians) to develop and implement innovative AI algorithms applied to medical images To lead effort on enabling translational and physician-in-the-loop AI solutions for medical
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at Northeastern. (10%) Required Qualifications: - Ph.D. in Human-Computer Interaction, Information Science, Computer Science, Design, or related fields - Strong record of published research in HCI, CSCW, DIS
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programming languages such as Python and C++, as well as experience with machine learning frameworks like TensorFlow or PyTorch Familiarity with image processing libraries and a solid grasp of deep learning