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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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novel biomarkers by integrating proteomics, metabolomics, and genomics / transcriptomics data with machine learning techniques. The position is to be filled starting November 1, 2025, either full-time or
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experts in machine learning, classical algorithms, and many-body quantum systems, with research topics including: Adiabatic and measurement-based quantum computing, Tensor networks for machine learning
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diseases and ageing, and have multiple national and international collaborations. Key Responsibilities: Perform tasks and support all aspects of the research project. Conduct accurate monitoring, documenting
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diseases and ageing, and have multiple national and international collaborations. Key Responsibilities: Perform tasks and support all aspects of the research project. Conduct accurate monitoring, documenting
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of reagents and supplies; optimize ordering and project scheduling to meet service expectations. Monitor instrument performance, oversee routine maintenance, and coordinate vendor support. Uphold SOPs, aseptic
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | 2 months ago
) the construction of machine or deep learning models to identify patterns of codon usage in yeast genomes, 2) the implementation of tRNA-sequencing across diverse yeasts and conditions, and 3) the construction
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the appointment. The candidates should have a strong track record in at least one of the group’s research areas, preferably demonstrated by publications in high-impact venues. Experience with machine learning
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postdoctoral fellow, committed to advancing inclusive and interdisciplinary science, to join an international team applying state-of-the-art machine learning technologies to stem cell and immune engineering in
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems