568 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"Bournemouth-University" positions at Nature Careers
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. Collaborate with interdisciplinary EIT Oxford teams to link fundamental cell-developmental genetics research to machine-learning models designed to augment the search for relevant target genes. Requirements
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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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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
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measure gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning
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peripherals). Experience supporting business teams around IT needs. Prefer experience with Windows, Apple, and Linux. Licensure, Registration and/or Certification Required by SJCRH Only: Certification in A
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peripherals). Experience supporting business teams around IT needs. Prefer experience with Windows, Apple, and Linux. Licensure, Registration and/or Certification Required by SJCRH Only: Certification in A
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Senior Semantics Data Scientist (m/f/d) in the fields of Computer Science, Data Science, Physics, Ma
key role in the foundation of interoperable, machine-readable data platforms, powering AI analyses and automated workflows. You will collaborate with top scientists from all subject areas at BAM
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tabletop, functional, and full-scale drills to test institutional preparedness. Participate in a critique of each drill record lessons learned, and develop improvement plans to address identified shortfalls