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methods for additive manufacturing applications, including ANNs and evolutionary genetic algorithms for process optimization supported by programming knowledge (e.g. Matlab, Python). Hands-on experience
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datasets to identify and characterize microbial and plant-derived biosynthetic pathways, predict their ecological functions, and reconstruct the (co-)evolutionary dynamics of traits such as microbiome
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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Job Description We are looking for a Research Fellow under the National University of Singapore (NUS) to support a project investigating the fundamental theoretical and algorithmic performance
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on a project in the area of cross-border electricity market. The role will focus on cross-border electricity market design, market risk analysis, bidding strategies, and relevant optimization algorithms
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++, or related languages Experience with high-performance computing or scalable algorithms Interest in interdisciplinary research spanning genomics and evolutionary biology Modes of Work The position is on-site in
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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | about 2 months ago
biology as a high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms. No prior biomedical training is required—just strong
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machine learning, deep learning, audio/image/video classification, attention mechanisms, zero/few shot learning, and evolutionary algorithms. The Research Associate should have proficient programming skills
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by applying existing and novel computational biology, bioinformatic, and machine learning algorithms to sequencing datasets and correlating them with multi-dimensional clinical datasets that contain
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(for example insect-eye–inspired motion detectors for planetary landing and insect-inspired navigation algorithms) to evolutionary and neuromorphic approaches to autonomous control, as well as soft-robotic