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the project. Strong research profile in the applications of machine learning, artificial intelligence, multi-objective optimization, spatiotemporal modeling, and processing of satellite and high-frequency flux
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techniques in a fast-paced environment with a strong team focus. This represents a unique opportunity to acquire a strong practical knowledge base in a broad range of highly desirable transgenic biology skills
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, multiscale modeling, molecular simulation code/software (e.g., LAMMPS, GROMACS), machine learning. Prior experience with applying simulations to biomolecular systems is a plus but not required. Applicants
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focuses on translational research at the intersection of bioelectronics, healthcare-focused nanofabrication, and emerging applications of machine learning in radiology. Our team operates within a state-of
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27708, United States of America [map ] Subject Area: Civil and Environmental Engineering Appl Deadline: (posted 2025/06/22, listed until 2025/12/22) Position Description: Apply Position Description A
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transcriptomics analysis • Interest in cancer biology and immunology principles • Excellent written and verbal communication skills Preferred Qualifications: • Experience with machine learning approaches
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, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases
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geometric understanding of training in deep neural networks. The position offers excellent training opportunities at the intersection of machine learning and applied mathematics. Qualifications: - Applicants
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
, and Precision Health. The project aims to leverage AI and machine learning (ML) to analyze complex metabolomics datasets and address key health challenges, including biomarker discovery, disease