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Job Purpose We are seeking a highly motivated Computer Scientist to advance tissue-based research through innovative computational and artificial intelligence (AI) approaches to pathological images
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/computational neuroscientist with a strong mathematical and computational background. Candidates with an interest in understanding the overlap and differences between natural and artificial intelligence
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, and clinical informatics, with healthcare innovation and industry partners. Our research on early prediction of dementia (https://www.bbc.co.uk/news/health-57934589 ) has the potential to deliver
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particular Word and Outlook. The ability to work effectively as part of a team, be able to prioritise work activities and have a flexible approach to work are essential attributes. What you will get in return
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This Employer https://main.hercjobs.org/jobs/22171221/teaching-fellow-in-computer-science-and-engineering-engineering-ay-26-27-includes-summer-2026-anticipated Return to Search Results
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California State University, San Bernardino | San Bernardino, California | United States | 11 days ago
communication skills with a strong teamwork orientation, ability to follow the direction of the program and the director's leadership. Must demonstrate proficiency in Microsoft Word, Excel, Outlook, and
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) Established Researcher (R3) Application Deadline 30 Mar 2026 - 12:00 (UTC) Country Italy Type of Contract To be defined Job Status Not Applicable Is the job funded through the EU Research Framework Programme
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, Communication, Computer Literacy, Confidential Data Handling, Epic EHR, Health Insurance Billing, Health Insurance Portability & Accountability Act (HIPAA), ICD-10 Procedure Coding System, Insurance Follow Up
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the Collaborative Doctoral Partnerships programme, training researchers at the science-policy interface. Where to apply Website https://jobs.unibas.ch/offene-stellen/phd-position-ai-driven-pathways-to-health
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/10.1007/s11142-024-09822-y . He, L.- Y., Wang, L. (2025). Can artificial intelligence curb greenwashing? Firm-level evidence based on large language model. Energy Economics, 152, 108954. https://doi.org