Post Doctoral Researcher at Sungkyunkwan University, Republic of Korea, South Korea
Dr. Sandeep Jain is a Postdoctoral Researcher at the Department of Materials Science and Engineering, Sungkyunkwan University, South Korea. With a solid foundation in materials science, Dr. Jain has gained extensive experience in alloy development, mechanical behavior analysis, and the application of machine learning in materials research. His work spans critical areas such as high-entropy alloys, phase equilibria studies, and the development of lightweight materials, making significant contributions to the field. He has collaborated internationally, demonstrating his ability to work across interdisciplinary teams and cutting-edge projects, driving innovation in materials engineering.
Profile
ORCID
Education:
Dr. Sandeep Jain holds a Ph.D. in MEMS from the Indian Institute of Technology (IIT) Indore, which he completed in 2023 with a CGPA of 8.67/10. Prior to his doctoral studies, he earned his Master of Technology in Material Science and Engineering from IIT Indore in 2017, achieving a CGPA of 8.75/10. His academic journey began with a Bachelor of Engineering in Mechanical Engineering from Jai Narain Vyas University, Jodhpur, in 2013, where he secured a percentage of 68%. Dr. Jain’s academic excellence in these esteemed institutions reflects his dedication to research and innovation in materials science.
Experience:
Dr. Jain’s professional experience includes his current position as a Postdoctoral Researcher at Sungkyunkwan University, where he is involved in the design and development of lightweight multicomponent alloys using machine learning. Prior to this, he worked as a Research Associate at IIT Delhi, where he focused on the mechanical and creep behavior of Ni-based superalloys and XRD analysis of hot-rolled aluminum-containing stainless steel. Additionally, he has served as a Project Associate at IIT Indore, contributing to the design and development of lightweight Ni-based alloys. During his Ph.D., Dr. Jain conducted in-depth research on phase equilibria and mechanical properties of multicomponent alloys, further enhancing his expertise in the field.
Research Interest:
Dr. Jain’s research interests lie at the intersection of materials science and machine learning. He specializes in the development of high-entropy alloys for high-temperature and high-strength structural applications, phase equilibria studies, and mechanical behavior analysis. His work also extends to solidification simulation and materials characterization, where he applies machine learning techniques to optimize alloy performance and predict mechanical properties. His interdisciplinary approach bridges advanced materials research with emerging technologies, making his work highly relevant for the future of engineering materials.
Awards:
While Dr. Jain’s CV does not list specific awards, his accomplishments in cutting-edge research, prestigious academic affiliations, and numerous publications in high-impact journals demonstrate his growing recognition in the field of materials science. His contributions, particularly in the integration of machine learning with alloy development, reflect a forward-thinking approach, positioning him as an emerging leader in his field.
Publications:
Dr. Jain has an impressive list of publications in reputed journals, covering a wide range of topics from phase equilibria to machine learning in material science. Some of his notable works include:
Phase equilibria and mechanical properties in multicomponent Al-Ni-X (X= Fe, Cr) alloys (2018), Trans Indian Inst Met, cited by 40.
Phase evolution and mechanical behavior of Co-Fe-Mn-Ni-Ti eutectic high entropy alloy (2018), Trans Indian Inst Met, cited by 30.
Solidification simulation of single-phase Fe-Co-Cr-Ni-V high entropy alloy (2022), Philosophical Magazine, cited by 25.
Effect of Si on microstructure and mechanical properties of Al-Cu alloys (2022), Silicon, cited by 20.
Solidification simulation of 6-component single-phase high entropy alloy (2022), Trans Indian Inst Met, cited by 15.
Effect of Ni and Si alloying elements on phase evolution and mechanical properties of Al-Cu alloys (2023), Material Chemistry and Physics, cited by 18.
Effect of Friction Stir Processing of novel designed Aluminum-Based Alloys to Enhance Strength and Ductility (2023), Arabian Journal for Science & Engineering, cited by 14.
Prediction of hot deformation behavior in AlCoCrFeNi2.1 eutectic high entropy alloy by conventional and artificial neural network modeling (2023), The Transactions of Indian National Academy of Engineering, cited by 12.
Effect of Ta on the evolution of phases and mechanical properties of novel seven components Fe-Co-Ni-Cr-V-Al-Ta eutectic high entropy alloys: Experimental study and Numerical Simulation (2024), The Transactions of Indian National Academy of Engineering, cited by 10.
Prediction of the effect of Ta on the mechanical behavior and experimental validation of novel six components Fe-Co-Ni-Cr-V-Ta eutectic high entropy alloys (2024), Refractory Metals and Hard Materials, cited by 8.
Enhancing flow stress predictions in CoCrFeNiV high entropy alloy with conventional and machine learning techniques (2024), Journal of Material Research and Technology, cited by 6.
A Machine Learning Perspective on Hardness Prediction in Advanced multicomponent Al-Mg based Lightweight Alloys (2024), Material Letters, cited by 4.
Genetic Algorithm Optimized Multiply Strategies Flow Behavior Modeling at Elevated Temperatures: A Case Study of AA6061-T6 Alloy (2024), Journal of Material Research and Technology, cited by 2.
Conclusion:
Sandeep Jain stands out as an innovative and productive researcher with expertise in materials science, machine learning integration, and alloy development. His accomplishments in publishing high-impact research, international collaborations, and the application of advanced techniques like machine learning in engineering materials make him a strong candidate for the Best Researcher Award. However, broadening his research impact and taking on more leadership roles could further elevate his candidacy in the future.