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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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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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using machine learning and deep learning techniques to generate indicators that allow remote monitoring of restoration. Knowledge of remote sensing (e.g. GEDI, LiDAR, multispectral) and programming (e.g
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, implement, and evaluate computational models that assimilate 2-photon data (60%) Use a computer programming language to create novel neural network simulations (models) that include realistic simulations
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d) excellent written, oral communication skills e) strong data analysis skills. Ideal applicants will also have experience with some combination of: a) Machine learning e) code optimization and
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage
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. Experience with high-throughput molecular biology assays. Experience with complex functional experiments. Background in machine learning, AI, or data integration for genomic datasets. Familiarity with gene
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investigate how machine-learning based algorithms can be used to personalize the user experience. The goal of this personalized user experience is to enable each individual user to discover their own
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Postdoctoral Research Associate in Machine Learning ( Job Number: 25000466) Department of Computer Science Grade 7: - £38,249 - £45,413 per annum Fixed Term - Full Time Contract Duration: 24 months
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, Economics, or a related field, earned within the past six years Strong computational and statistical skills Experience with large-scale data analysis and machine learning Proficiency in scientific programming