360 machine-learning "https:" "https:" "https:" "https:" "https:" "U.S" research jobs at Nature Careers
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Experience in event planning and database management is preferred Compensation In recognition of certain U.S. state and municipal pay transparency laws, St. Jude is including a reasonable estimate of the
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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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provided) Ability to work in a fast-paced, team-oriented environment Strong organizational and communication skills Experience with cell culture is a plus Interest in learning new technologies Minimum
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the home laboratory. Proven performance in earlier role/comparable role. Compensation In recognition of certain U.S. state and municipal pay transparency laws, St. Jude is including a reasonable estimate of
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environments Interest in industrial monitoring systems, smart sensors, and sustainable manufacturing Experience with sensor data processing or instrumentation systems Knowledge of machine learning or anomaly
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within the project AI4TECSWriting a doctoral dissertation in computer sciencePublishing research findings in leading international conferences and high‑impact journals in AI, machine learning, and
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qualifications include: Ph.D. in Computer Science, Computer Engineering, Electrical Engineering or a related field; Strong background in Deep Learning (e.g., Transformers, foundation models); Strong programming
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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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human health. Within this mission, the Iorio Group works at the intersection of computational biology, functional genomics, and precision oncology, integrating machine learning, large-scale CRISPR
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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