University College of Engineering (A), Kakinada, JNTUK invites applications from qualified candidates for the post of Research Fellow under a Ministry of Electronics & IT (MeitY), Govt. of India sponsored project. The project titled “AI-Driven Solution for Autonomous Cybersecurity Threat Detection, Analysis and Mitigation” is hosted by the Department of Computer Science and Engineering.
Job Details
| Attribute | Information |
| Hiring Institution | JNTUK, Kakinada, Andhra Pradesh |
| Sponsoring Agency | Ministry of Electronics & IT (MeitY), Govt. of India |
| Open Position | Research Fellow |
| No. of Posts | 02 Positions |
| Project Title | AI-Driven Solution for Autonomous Cybersecurity Threat Detection, Analysis and Mitigation |
| Hosting Department | Department of Computer Science and Engineering, UCEK |
| Monthly Emoluments | ₹37,000/- p.m. + HRA (or ₹42,000/- p.m. + HRA for candidates with 2+ years research experience) |
| Tenure | Purely temporary & co-terminus with project tenure |
| Application Deadline | September 17, 2026 by 05:00 PM |
About Institution
JNTU Kakinada (University College of Engineering) is a premier state autonomous institution offering high-quality engineering education and advanced scientific research in Andhra Pradesh.
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Job Description
The selected Research Fellows will design, implement, and validate artificial intelligence and machine learning models for automated threat detection, log analytics, and intrusion mitigation within modern SOC/SIEM environments.
Requirements & Required Qualifications
1. Essential Qualification
- M.Tech. / M.E. / MS (Research) / 5-year Dual Degree in Computer Science / IT / Cyber Security / Artificial Intelligence / Data Science / Information Security or equivalent.
- Minimum of 60% marks or equivalent CGPA.
2. Mandatory Technical Skills (At least two preferred)
- Machine Learning / Deep Learning model development.
- Python programming with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Cybersecurity analytics, intrusion detection, or log analysis.
- Data preprocessing, model validation, and performance evaluation.
3. Desirable Criteria
- Hands-on experience with Security Operations Center (SOC) tools, SIEM platforms, or cyber datasets.
- Past research publications or project experience in AI/Cybersecurity.
- Familiarity with multiple operating systems.
Roles and Responsibilities
- AI Model Development: Build and train Machine Learning/Deep Learning classifiers using PyTorch/TensorFlow to detect threat vectors.
- Log & Intrusion Analytics: Preprocess security logs and analyze network traffic datasets.
- Benchmarking: Validate model performance, document metrics, and publish findings.
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Skills You’ll Gain
| Category | Specific Tools & Competencies Mastered |
| Machine Learning / AI | PyTorch, TensorFlow, Scikit-learn, Threat Detection Classifiers. |
| Cybersecurity Operations | SIEM Platforms, SOC Workflows, Log Analysis, Intrusion Detection Systems. |
| Data Science & Validation | Cyber Dataset Preprocessing, Model Validation, Performance Metrics. |
Application Procedure & Selection Process
Application Instructions
- Submit your application online via the official Google Form: Apply Here.
- Required Attachments: Detailed CV (maximum 3 to 4 pages), scanned copies of mark sheets (from Class X onwards), and experience certificates.
- For official notifications, visit the JNTUK Official Notifications Portal.
Selection Details
- Shortlisted candidates will be notified by email.
- Final selection will be based on an in-person interview at JNTUK, Kakinada.
- For queries, email
asnchakravarthy@jntucek.ac.in.
Frequently Asked Interview Questions (With Answers & Solution Guidelines)
Machine Learning in Intrusion Detection
- Question:“How do you handle severe class imbalance when training ML algorithms on network intrusion datasets like NSL-KDD or CICIDS?”
- Answer & Solution Context: Detail using resampling techniques like SMOTE (Synthetic Minority Over-sampling Technique) or ADASYN, selecting cost-sensitive learning models, and using precision-recall AUC rather than raw accuracy to evaluate true detection efficacy.
SOC & SIEM Integration
Answer & Solution Context: Explain normalization protocols like CEF (Common Event Format) or JSON structuring, where unstructured syslog streams are parsed into standard key-value fields (source IP, destination IP, event ID) for downstream correlation rules or ML pipelines.
Question:“How do real-time SIEM tools structure event logs for automated threat analysis?”