213 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" positions at Nature Careers in United States
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has a passion for continuing to push the boundaries of our understanding. Candidates who demonstrate responsibility, initiative, and a strong drive to learn and succeed in a collaborative environment
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expression, cell state-specific regulatory programs, and clinical outcomes. Related projects will include: Develop and apply statistical or machine learning approaches to model the effects of common and rare
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Postdoctoral Research Associate - Hybrid Computational-Experimental Scientist in Bacterial Drug Resp
to antibiotics and host-like conditions. • Develop and apply statistical or machine-learning methods for interpreting single-cell and genomic datasets. • Work closely with wet-lab scientists to design perturbation
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attention to detail. This entry-level role is ideal for someone with prior undergraduate lab experience who is eager to learn and develop technical skills. The successful candidate will have some lab
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management. Demonstrated experience in one or more applied computational fields: application of modern machine learning methodology, algorithms, computational modeling, finite element analysis, computational
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. This project will involve applying and evaluating statistical and machine learning models for data integration and interpretation. A strong foundation in statistical modeling will be essential for applications
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methodologies: optogenetics, calcium imaging, viral tracing, tissue clearing, murine behavioral phenotyping, machine-learning behavioral analysis Familiarity with programming languages (e.g. R, Python) and an
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neuroimaging or behavioral studies, and strong communication skills. Additional expertise in computational neuroscience and data analysis, particularly using machine learning approaches, is highly desirable
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funding, and collaborative culture make it ideally suited to take this bold leap forward. To learn more about the initiative, visit here . About the role: We are seeking a highly motivated Research
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, immersive learning environment, and interdisciplinary collaboration across the natural and social sciences, engineering, and policy. DUML fosters a close-knit, hands-on academic community and offers