Academia-industry partnerships can play a transformative role in making India a manufacturing hub
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Yes, there is definitely a shift in trend. While hiring in the services sector—such as finance, IT, and banking—continues, we are seeing growing interest from companies in core sectors, including automobile manufacturing, components, and medical instruments. That said, the majority of students are not yet heading into hardware-related companies. It is a positive sign, though, indicating an emerging balance between the service and manufacturing sectors.More than 50per cent of India's economy is supported primarily by the services sector, including finance, IT companies, banking, and others. The country must also be seen as a global supplier of hardware. While India can manufacture iPhones and other electronic devices for the world, we lack a robust semiconductor foundry. One critical area is the sourcing of raw materials and basic components.Our services sector is strong, but for sustainable economic growth and asset creation, manufacturing must grow. Our students can design chips, but converting VLSI designs into domestically manufactured chips remains a dream. We need to step up as a global hardware supplier. Manufacturing requires long-term investment and patience–unlike software, where results can be quick. Companies must be willing to invest with a long-term vision.India is among the top three countries in knowledge generation, based on high-quality research publications; however, most of this activity is occurring on a small scale. For instance, we are doing graded additive manufacturing, which could be of use to organisations such as ISRO or DRDO, but translating it even to a prototype scale is rare. This is where academia-industry partnerships is important.As per UGC recommendations, we have launched a four-year BTech programme in AI and ML. These technologies will touch every sphere–from medicine to climate science. But introducing a programme is not difficult; the real challenge is finding competent faculty to teach it. Computer Science in India evolved—AI will, too. We must embed AI, ML, and data-driven tools into existing engineering disciplines. Only then will we build a strong, sustainable talent pipeline.AI should support, not replace, core engineering knowledge. For example, a thermodynamics course should also teach AI-based simulation tools; however, AI does not create new knowledge; instead, it works on existing data. To feed AI, we must generate new data through real-world experiments. This again highlights the need for hands-on learning, skilled faculty, and strong integration of AI with traditional engineering education.Space and infrastructure are significant challenges. The institute was established 70 years ago, and most of its infrastructure dates back to the 1950s. Student intake has grown fourfold, but infrastructure hasn't kept pace. We require more equipment, additional space, and updated tools so that students can truly engage in their learning.
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