Yes, you can give the same course (e.g., Biotechnology) in different categories with different priorities in VIT choice filling .

• VIT allows you to select the same branch under different fee categories (Category 1, 2, 3, 4, etc.) in different priorities.

• This is a common strategy used by students to improve their chance of getting the desired branch, even if it's in a higher fee category.

For more details, you can refer to VIT counseling guide at:
VITEEE Counselling Process at Careers360

With a VITEEE 2025 rank of 10,099 , securing admission to the Biotechnology program at VIT Vellore under Category 1 is unlikely. Based on the latest data, the expected Category 1 cutoff rank for Biotechnology at VIT Vellore ranges between 14,200 and 14,500 . (https://www.collegedekho.com/news/vit-vellore-category-1-expected-cutoff-rank-2025-65450/?utm_source=chatgpt.com)

Your rank positions you well within the expected cutoff for Category 2 admissions in Biotechnology.
Consider applying to VIT Chennai , VIT-AP , or VIT Bhopal .

VITEEE Cutoff 2024 (Expected) - Category wise Cutoff

With your VITEEE rank of 32,751 , you are eligible for counselling at VIT Chennai but may not get core branches like CSE (which typically require ranks below 25,000 ).

• Consider courses like Mechanical Engineering , Civil Engineering , or newer branches with higher cutoffs.

• Participate in counselling to explore options.

• Consider VIT Bhopal/Amaravati if VIT Chennai is difficult.

For more info, check: VIT Admissions (https://www.vit.ac.in/admissions) .

The Smart Manufacturing specialization in Mechanical Engineering at VIT Vellore, especially when combined with a minor in AI & ML, can offer a strong interdisciplinary profile with good career and higher studies prospects. Here’s a detailed look at the scope from both an academic and industry point of view:

At VIT, this specialization likely includes:

Advanced manufacturing processes

Industrial automation and robotics

IoT in manufacturing

Digital twin and cyber-physical systems

Data analytics for predictive maintenance