StartupNest: A Full-Stack Cloud Platform Enabling Student–Startup Workforce Integration
DOI:
https://doi.org/10.32628/IJSRST2613328Keywords:
Student–Startup Collaboration, Cloud Computing, Full-Stack Platform, Internship Management, Workforce Integration, Experiential LearningAbstract
The rapid growth of the startup ecosystem has created a strong demand for flexible, cost-effective workforce solutions, while students pursuing higher education seek opportunities to gain practical experience and improve employability. However, existing job portals and internship platforms primarily focus on full-time employment or structured corporate internships, making them unsuitable for early-stage startups and students interested in short-term or project-based roles. This paper presents StartupNest, a full-stack cloud-based platform designed to enable structured collaboration between students and startups. The system allows startups to post internship, project-based, and early-career opportunities, while students can explore roles, submit applications, and track their application status through dedicated dashboards. The platform incorporates role-based authentication, secure data storage, and cloud deployment to ensure scalability, accessibility, and data integrity. Additionally, intelligent features such as skill-based matching enhance the efficiency of connecting students with suitable opportunities. The proposed system demonstrates an effective, scalable, and user-centric approach to bridging the gap between student talent and startup workforce requirements, thereby supporting experiential learning and improving talent acquisition for startups.
Downloads
References
S. Choudhury and S. Bhowmick, “Student Internship Management System Using Web Technologies,” International Journal of Computer Applications, vol. 180, no. 24, pp. 1–5, 2018.
R. Jain and S. Jain, “A Study on Online Recruitment Systems and Their Impact on Hiring Efficiency,” International Journal of Engineering Research & Technology, vol. 6, no. 5, 2017.
N. Shah, “Cloud-Based Job Portal System,” International Journal of Advanced Research in Computer Science, vol. 9, no. 2, 2018.
M. Armbrust et al., “A View of Cloud Computing,” Communications of the ACM, vol. 53, no. 4, pp. 50–58, 2010.
P. Mell and T. Grance, “The NIST Definition of Cloud Computing,” National Institute of Standards and Technology, 2011.
Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” IEEE Computer, vol. 42, no. 8, pp. 30–37, 2009.
X. Amatriain and J. Basilico, “Recommender Systems in Industry: A Case Study,” in Recommender Systems Handbook, Springer, 2015.
T. Davenport and R. Ronanki, “Artificial Intelligence for the Real World,” Harvard Business Review, 2018.
K. Schwab, “The Fourth Industrial Revolution,” World Economic Forum, 2016. (Used for startup ecosystem growth context)
J. Q. Gan and S. Li, “Design and Implementation of Online Job Recruitment System Based on Web Technology,” International Journal of Computer Science Issues, vol. 9, no. 3, 2012.
A. Gupta and R. Saxena, “Cloud-Based E-Recruitment System for Efficient Hiring Process,” International Journal of Computer Applications, vol. 162, no. 3, 2017.
M. A. Hossain and M. S. Islam, “An Intelligent Job Recommendation System Using Machine Learning,” IEEE Access, 2019.
S. K. Sood, “A Combined Approach to Ensure Data Security in Cloud Computing,” Journal of Network and Computer Applications, vol. 35, no. 6, pp. 1831–1838, 2012.
World Bank Group, “Digital Platforms and the Future of Work,” World Development Report, 2019.
A. Singh and P. Mehta, “Digital Platforms for Internship and Skill Development: A Review,” International Journal of Emerging Technologies, 2020.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Scientific Research in Science and Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0