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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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are seeking a highly motivated researcher with a strong background and interest in machine learning and artificial intelligence. The position focuses on the development and application of advanced deep learning
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),. Familiarity with data analytics, machine learning, digital twin knowledge, or Python programming language. Knowledge of additive manufacturing, process physics, thermodynamics, and/or metallurgy to interpret
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methylation, chromatin accessibility), and clinical data. · Develop, apply, and benchmark machine learning and statistical models for subtype discovery, classification, and outcome prediction. · Contribute
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facilities and access to microscopes etc. The BioMS group is world-renown for mass spectrometry-based proteomics and structural biology, and applies these technologies to study amongst others cancer, auto
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on type, size, location of data centers in the US, their electricity and water demand, carbon emissions; exposure to air pollution. ● Develop and/or apply methods for causal inference and machine learning
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shaping microbial evolutionary trajectories. You will work on this novel project and use state-of-the-art mathematical/computational modelling and machine learning techniques in a collaborative working
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of Dentistry, Oral & Craniofacial Sciences at King's College London. Our methodological work spans trustworthy AI, multimodal machine learning and statistical signal processing. Our translational work applies
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biophysics, computational biology, mathematics in the life sciences, computer science and machine learning with application to biological systems, and related areas. What we seek The ELBE program seeks
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. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful