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. Experience in one or more of the following is highly desirable: Operating high-resolution mass spectrometry platforms (e.g. Orbitrap, AP-MALDI) Handling and analysing large-scale metabolomics datasets Data
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equivalent research experience. You will have a strong foundation in epidemiological and statistical research methods, with demonstrable experience in analysing longitudinal data and working with large-scale
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. adipose–liver–gut cross‑talk Support benchmarking of in‑vitro findings against clinical and patient‑derived data This role provides an exceptional opportunity to work at the cutting edge of human disease
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Business School and global advisory firm Ernst & Young (EY). The programme explores the organisational conditions and leadership practices that enhance the success of large-scale corporate transformations by
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in protein chemistry, protein engineering, and 3D structure determination of large/multi-subunit membrane enzyme complexes. Proven expertise in cryo-EM experiment design, execution, and data
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, procurement, or grant administration will be an added advantage. Experience in areas such as Machine Learning, Deep Learning, Computer Vision, Large Language Models, Data Analytics, or Intelligent Systems will
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psychology, human-computer interaction, and computer-mediated communication. The successful candidate should demonstrate expertise in computational social science methods and experience working with large
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
, biomedical engineering, computer science, epidemiology, or a related quantitative discipline. Demonstrated experience with large-scale human genetic data analysis (GWAS pipelines, QC, mixed models, population
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Fruiting Vegetables / Seed Innovation Hub. The work would include: Large-scale Global Germplasm Evaluation and Assessment, Key Deep Phenotypic Definition and Evaluation, including molecular, cell culture
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reasoning in Large Language Models (LLMs). • Developing, evaluating, and refining Retrieval-Augmented Generation (RAG) architectures that ground LLM outputs in DfS domain knowledge and project data