John Patrick O'Connor

Senior Mechanical Design Engineer at Fertilis

John Patrick O'Connor has a strong background in mechanical engineering and product development. John Patrick has worked in various roles, gaining experience in different industries.

Starting in 2023, O'Connor worked as a Senior Mechanical Design Engineer at Fertilis, a position they currently hold. Prior to that, they were employed as a Structural Mechanical Engineer at Fleet Space Technologies starting in September 2022.

From October 2021 to September 2022, O'Connor worked as an Anatomical Engineer at Fusetec. Before that, they held a position at Johnson & Johnson as a Senior 3D-Printing Manufacturing Engineer. At Johnson & Johnson, O'Connor was involved in every aspect of the product process development, specializing in CAD and 3D-printing technologies.

In previous roles, O'Connor served as a Research Associate at Elvesys, where they worked on a European Union project investigating biomarkers for dry-eye disease. John Patrick also worked as a Product Development Engineer at Microsemi Corporation, specializing in the manufacturing and qualification of semiconductor components.

O'Connor's work experience also includes their time as a Ph.D. student at the University of Limerick, where they specialized in heat transfer and fluid mechanics. During their doctoral studies, they also worked as a Graduate Teaching Assistant, providing support in engineering modules.

In addition, O'Connor gained experience as an intern at Alcatel-Lucent and Abbott Laboratories, where they contributed to product development and manufacturing engineering projects.

Overall, John Patrick O'Connor has a diverse range of experience in mechanical engineering, specializing in areas such as 3D-printing, product development, and heat transfer.

John Patrick O'Connor pursued their higher education from 2007 to 2011 at the University of Limerick, where they earned a Bachelor of Engineering degree in Mechanical Engineering. Recently, in April 2021, they obtained a certification in Machine Learning from Coursera.

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