386 machine-learning-"https:"-"https:"-"https:"-"https:" Fellowship positions in United States
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successful candidate, you will: This project with Dr. Nagarajan Vaidehi involves developing and application of interpretable machine learning methods to uncover allosteric regulation of disordered regions in
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analysis, PDE-constrained optimization and optimal control, numerical analysis and scientific computing risk-averse and fractional models, digital twins and data-assimilating models, machine learning and
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Qualifications Experience with machine learning and AI models. Ability to communicate clearly with colleagues, Data presentations Scientific publication experience Special Instructions Priority Application Review
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research projects in computer vision, machine learning, AI, and robotics. Projects may include physically-grounded AI guidance agents, modeling of multimodal data, and generative AI systems for situated
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across the following research areas: Predictive machine learning Robust and stochastic optimization Learning-enabled control and reinforcement learning Power system operations, planning, and electricity
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to have a strong background in the foundations of machine learning. Special Instructions Required application documents include a cover letter, CV, a statement of research interests, and up to three
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microbiome studies, microfluidics, or/and Machine-Learning to join our dynamic research team. The successful candidate will play a pivotal role in advancing our understanding of microbial communities through
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such as: Causal inference and the design and analysis of experiments Reinforcement learning and sequential decision-making Analysis of complex systems, networks, and large-scale data Machine learning
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experience in working with Linux HPCs · Experience in applying machine learning methods to genomics data analysis · Experience in navigating public databases and genomics data repositories
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automation, computational drug design, machine learning, and software engineering. The ideal candidate will contribute to innovative research and the development of advanced computational tools within our lab