MaterialsZone
Omri Schulman has a diverse work experience in various industries. Omri is currently working as a Solutions Engineer at MaterialsZone since March 2023. Prior to that, they worked as an R&D Process Researcher at Nano Dimension from July 2021 to March 2023.
Before joining Nano Dimension, Schulman served as a Materials R&D and Process Engineer at the Israeli Ministry of Defense from December 2016 to June 2021. Omri also gained practical experience as a Student R&D Internship at Teva Pharmaceuticals from June 2016 to November 2016.
Schulman began their career in research as a Student Research Assistant at the Technion - Israel Institute of Technology from May 2015 to July 2016. Overall, they have developed a strong background in materials research, process engineering, and solutions engineering through their work in both academic and industry settings.
Omri Schulman completed their education at several institutions. From 2012 to 2016, they attended the Technion Institute of Technology, where they earned a Bachelor of Engineering degree in Chemical Engineering. Moving on, they pursued higher education at Tel Aviv University from 2018 to 2022, obtaining a Master's degree in Materials Engineering. Prior to their university studies, from 2008 to 2011, they attended the International School of Bangkok and completed the International Baccalaureate program.
MaterialsZone
Materials Zone has developed a materials discovery platform that funnels R&D and production data into an interoperable and structured database, enabling users to efficiently collaborate, manage work processes, achieve meaningful AI/ML insights, and drive better decision-making. The company’s solution is designed to help users acceleratetheir R&D by discovering new and better materials. The platform uses ML guidance and materials informatics to forecast outcomes and achieve faster and improved results. The Materials Zone solution allows users to create models on the way to production; to scale up and test the limits of their models in order to design the most cost-efficient and robust production lines; and to reduce production failures by using models to predict future failures based on supplied materials informatics and production line parameters.