599 machine-learning-"https:"-"https:"-"https:"-"https:"-"The-Open-University" positions at Nature Careers
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applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department, e.g. seminars, workshops and schools organised by the members
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(e.g. Nextflow) and cloud compute environments (e.g. OCI, AWS, GCP) Familiarity with Bayesian methods, machine learning, or causal inference in the context of biological data Contributions to open-source
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, disability, age, ancestry, military service, protected veteran status, or other characteristics protected by law. Pay Transparency Statement The hiring range is based on market pay structures, with individual
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About the School SDEM integrates technology, economics, and management to cultivate versatile, innovation-driven leaders with global perspective. Hiring Clusters · Data Science & AI: machine learning
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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials
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related field. Demonstrated Expertise in one or more of the following areas: Bio and AI: Theoretical and computational biophysics Machine learning and data analysis for biological systems Biomedical imaging
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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tumor cells and host or immune cells in the tumor microenvironment and periphery to identify predictive biomarkers and develop novel immunotherapeutic approaches for solid tumors (e.g., TCR T cells, CAR T
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artificial intelligence, machine learning, and the life sciences to shape the future of data-driven biology and biomedicine. We are seeking visionary researchers whose work pushes the boundaries of AI-enabled
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glycoproteomics, including data analysis Experience in metabolomics, including data analysis Experience in lipidomics, including data analysis Experience with machine learning in proteomics data analysis