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Field
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learning, and computational biology to adapt and augment existing open models for predictive biology. Approaches may include fine-tuning, parameter-efficient tuning, probing, and retrieval- or knowledge
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Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description
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distribution. Both projects will develop the Fontan emulator to be patient-specific, MRI compatible, computer controlled, and quantitatively evaluated using advanced bench-top, CFD/FSI simulation, and MRI-based
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School of Public Health studies the frequency, distribution, and determinants of disease in humans, a fundamental science of public health. In addition to pursuing ground-breaking global research
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to federal research proposals, sponsored research applications, and collaborative project reports. Familiarity with scalable computing environments, cloud platforms, high-performance computing, distributed AI
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scientific domains. Preferred Experience: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling
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, distributed control, formation control, perception, and decision-making in complex environments. Design and implement data-driven approaches, including machine learning, reinforcement learning, surrogate models
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Yeshiva University to enhance teaching Experience & Educational Background: Ph.D. in Computer Science, focused on Artificial Intelligence or Distributed Systems Candidates in Cybersecurity, Data Management
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to engage with the eBird Status and Trends modeling team, access to high-performance computing resources, and opportunities to collaborate with researchers from other programs at Cornell University. In
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efforts across scientific systems. Develop and apply federated learning on distributed and heterogenous datasets. Develop more efficient and resilient DP techniques that minimize performance loss while