About the Role
The Paid internship Program at Krishna Research Foundation (KRF) is designed for students and graduates who aspire to enhance their research skills through structured mentorship and hands-on learning. Interns will engage in simulation-based and data-driven research projects under the guidance of experienced mentors, gaining practical exposure to academic and technical research workflows.
Responsibilities
- Assist in developing and executing projects using Python, MATLAB, NS2/NS3, SPSS, or Excel.
- Support data analysis, documentation, and simulation activities.
- Participate in weekly project reviews and research training sessions.
- Contribute to plagiarism checks, formatting, and citation management.
Qualifications
- Bachelor’s or Master’s students in Engineering, Computer Science, Data Science, Cybersecurity, Artificial Intelligence & Machine Learning (AI & ML), or related disciplines.
- Familiarity with Python, MATLAB, NS2/NS3, Excel, or SPSS is preferred.
- Interest in academic writing, data analytics, and simulation-based research.
Mode and Duration
- Mode: Offline
- Duration: 3 to 6 months
Fees and Structure
- Internship Fee: ₹10,000 per month (for 3–6 months).
- Fee includes access to mentorship, research tools, simulation environments, and evaluation support.
- Certificate of Completion and Recommendation issued upon successful completion.
Perks and Benefits
- Work on live institutional and simulation-based research projects.
- Receive personalized mentorship and continuous project feedback.
- Access to research skill-building workshops and documentation guidance.
- Certificate and official recommendation letter from KRF.
General Guidelines for All Interns
- Maintain professionalism, discipline, and ethical standards during the internship.
- Progress will be evaluated through bi-weekly mentor reviews.
- Certificates are issued only after successful completion of assigned deliverables
Selection Process
The Paid internship Program is open to candidates seeking to enhance their research skills through structured mentorship without a competitive screening process.
- Candidates are enrolled based on domain preference, learning intent, and available mentorship capacity.
- Admission will be confirmed after review of the application and discussion of research goals.
This program focuses on guided self-learning, project-based engagement, and professional development under expert supervision.
