MaterialsZone
Nickolay Pavliukov has a diverse background in web development, starting in 2014. Nickolay worked as a Front-End Developer at 42devstudio from April 2014 to October 2014. Following that, they became a Full Stack JavaScript Developer at BIFIT from October 2014 to May 2015, where they worked on projects including ibank employee room. Nickolay then worked as a Front-End Developer at workrocks from May 2015 to December 2015, where they contributed to projects such as a binary contracts trading platform. In 2015, Nickolay joined Faame as a Full Stack JavaScript Developer, focusing on their browser streaming platform until September 2016. Nickolay then joined Tundra Mobile in November 2016 as a Front-End Developer, working on the roomster.com project. Nickolay later became a ReactJS Developer at Intellica Group from December 2016 to February 2018. Currently, they work at Materials.Zone as a Frontend (React) Developer, contributing to projects that enable collaborations in research and industry.
Nickolay Pavliukov completed their education at two different institutions. From 2005 to 2008, they attended \u0414\u041e\u041b\u0418\u0424\u041c\u041f (physical-mathematical lyceum), where details of their degree and field of study are not provided. Subsequently, they studied at Dnipropetrovs'kij Nacional'nij Universitet from 2008 to 2012, earning a Master's degree in mechanical-mathematics.
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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.