Brittany Nutt

Machine Vision Mechanical Design Engineer at Catalyx North America

Brittany Nutt is a skilled Machine Vision Mechanical Design Engineer at Catalyx North America, with experience in developing mechanical designs for automated systems and leading machine vision projects presented at PACK EXPO 2022/2023. Brittany has expertise in creating CAD models and mechanical drawings, managing client technical documentation, and executing installation and acceptance testing of mechanical systems. Prior to this role, Brittany gained valuable experience as a Biomaterials R&D Co-Op at DSM, focusing on polymer orthostructural products and FDA testing for medical implants. Brittany holds both a Master of Science and a Bachelor of Science in Mechanical Engineering from Drexel University.

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Philadelphia, United States

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Catalyx North America

Catalyx, formerly CXV Global (Crest Solutions, Xyntek and Vistalink) and Panacea Technologies, are specialists in the science of operational processes. With a footprint across North America and Europe, we carefully automate and service R&D, production, packaging, and delivery processes—globally—with a sophisticated blend of products, technologies, and professional services. For over three decades, our partnership-based culture has helped many of the world's leading production intense, high-regulated industries optimize operational efficiency, performance, and safety seeing them through to their success. For more information visit www.catalyx.ai Core Capabilties are: Automation Solutions: • Inspection • Validation • Verification • Process automation • Procurement and design • IT/ OT integration and security, • Architecture design and deployment Services: • Functional Service Provision • Managed Services • Service Level Agreements • Spare parts • Equipment • Software and solution maintenance • Training and Consultancy. Powered by our award-winning solutions and platforms; SmartFactory Software Suite OpenBIO™ Digital LineClearance Assistant™ Machine Vision Integrated Robotics AI & Deep Learning Data Analytics Traceability.


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501-1,000

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