313 phd-studenship-in-computer-vision-and-machine-learning Postgraduate research jobs
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of an accredited Medical Assistant program or at least one year as a Medical Assistant or CCMA or NCRMA certification. Certifications/Licenses: Basic Life Support (BLS) certification Preferred Qualifications
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partners in Thailand and Australia in a project funded by the British Council, and you will be responsible for investigating the latest developments in computer vision and Deep Learning to develop powerful
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Salary Tuition Benefits Internal Training Medical insurance – including 100% paid employee medical coverage for full-time employees Dental Insurance Vision Insurance Long and short-term disability
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access to various professional development opportunities, including a membership to Academic Impressions, LinkedIn Learning, and UT Dallas Bright Leaders Program. Visit https://hr.utdallas.edu/employees
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will have desk space and computer access. Organizational Status The individual will work under close supervision of a Senior Research Associate. The individual will report on a regular basis to the
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of exceptional benefits. Benefits eligible employees enjoy a wide array of benefits including comprehensive medical, prescription, dental, and vision insurance as well as a generous retirement savings program
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, statistics, and machine learning – Experience in safe laboratory procedures. Effective verbal and written communication skills. Laboratory experience demonstrating attention to detail and commitment to safe
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| Essential skills/experience for the role To be successful in this role you will have: A relevant science qualification, ideally a PhD Proven experience in environmental research and data analysis Strong
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | 2 months ago
USNH Employees should apply within Workday through the Jobs Hub app Position for a graduate student or adjunct to analyze ground magnetic field perturbations using machine learning techniques
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supervision of a postdoctoral fellow to study breast cancer initiation and progression using a newly developed mouse model of breast cancer together with primary patient samples. You will additionally learn a