🚀 GE Aerospace is inviting applications for its Cyber Security Internship 2025 in Bengaluru! If you're a passionate engineering student currently pursuing your Bachelor's or Master's degree, this is your chance to gain hands-on experience at a global leader in aerospace innovation. This internship offers a unique opportunity to contribute to cutting-edge research in cybersecurity, AI, and machine learning that supports aerospace technology and operations. Work alongside top researchers and engineers to develop AI-driven systems and gain exposure to advanced recommendation engines, NLP, and industrial data challenges. Start your career with GE Aerospace and be a part of shaping the future of flight! ✈️💻
📋 Job Details
🧑💼 Role: Cyber Security Intern
🎓 Qualification: Bachelor's or Master's in Engineering (CS, ECE, EE, ME, Industrial, etc.)
🧑💻 Experience: Freshers / Currently Pursuing
📍 Location: Bengaluru
💰 Salary: ₹3.3 to ₹5 LPA (Estimated)
🗓️ Last Date: Apply ASAP
✅ Eligibility Criteria
🎯 Enrolled in a full-time Bachelor's or Master's/PhD program in Computer Science, Electronics, Electrical, Mechanical, Industrial, or related fields
🤖 Strong background in Natural Language Processing (NLP), Machine Learning (ML), or Artificial Intelligence (AI)
🐍 Proficient in Python for algorithm implementation and data processing
📊 At least one year of experience conducting independent research
💬 Strong communication skills and ability to work in fast-paced, ambiguous environments
🧾 Job Description
💡 Work with a team of researchers at GE Aerospace to develop search and recommendation systems for internal engineering tools
🧠 Design and implement AI algorithms that process complex aerospace domain data
🧪 Conduct experiments, evaluate model performance, and document findings for internal and external knowledge sharing
⚙️ Address real-world industrial challenges such as data quality, domain-specific terminology, and AI integration in regulated environments
📝 Opportunity to publish findings and contribute to patents
🌟 Why Join GE Aerospace?
✨ Work on next-gen aerospace innovations that redefine the future of flight
🌍 Be part of a global team shaping aviation safety, efficiency, and intelligence
🎓 Get mentored by world-class scientists at the John F. Welch Technology Center (JFWTC)
🚀 Gain real-world experience with AI, ML, and cybersecurity in critical systems
💼 Culture of innovation, transparency, collaboration, and leadership
🏢 About GE Aerospace – Driving the Future of Flight
GE Aerospace is a global leader in aircraft engine manufacturing and aviation technology, known for innovation in cybersecurity, AI, and engine design. At its Bengaluru R&D center (JFWTC), GE scientists contribute to aviation patents and breakthrough technologies like additive manufacturing and predictive analytics. GE Aerospace empowers fresh talent to revolutionize the skies with cutting-edge research and sustainable aviation solutions. 🌐🛫
📌 Other Highlights
🔍 Research-focused role with deep learning, NLP, and ML tasks
🧑🔬 Opportunity to work on Large Language Models (LLMs) and vision algorithms
🧾 Exposure to industrial-grade data systems in aviation
🧠 Build technical depth in computational architectures, AI pipelines, and real-world deployment
🛫 Contribute to safer, smarter, and more efficient aviation systems
📥 How to Apply?
All interested and eligible candidates can apply online using the official application link below:
👉 For More Details & Apply: Click Here
💬 Top 5 Interview Questions & Answers
Q1: What are recommendation systems and how are they used in cybersecurity?
🅰️ Recommendation systems suggest items based on user behavior. In cybersecurity, they can detect anomalies or recommend security measures based on threat intelligence.
Q2: Explain the differences between supervised and unsupervised learning.
🅰️ Supervised learning uses labeled data for training, while unsupervised learning identifies patterns in unlabeled data. Both are useful in anomaly detection and predictive security.
Q3: How would you fine-tune a pre-trained NLP model for aerospace data?
🅰️ Preprocess domain-specific data, then use transfer learning techniques like instruction tuning on a pre-trained NLP model, ensuring model relevance and accuracy.
Q4: What challenges might arise when applying AI to aviation or industrial domains?
🅰️ Challenges include data sparsity, strict regulatory constraints, safety-critical systems, and domain-specific vocabulary requiring tailored AI approaches.
Q5: What’s your approach to solving a new ML problem with limited data?
🅰️ Begin with data augmentation, leverage transfer learning, apply cross-validation, and consider semi-supervised learning if possible to maximize model performance.
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