Indian Institute of Science Education and Research (IISER) Tirupati, an Autonomous Institution of National Importance under the Ministry of Education, Govt. of India, invites applications from eligible Indian nationals for the position of Project Scientist-I. The temporary contractual role focuses on research in quantum algorithms, quantum annealing, and multi-modal optimization.
Job Details
| Attribute | Information |
| Hiring Institution | Indian Institute of Science Education and Research (IISER) Tirupati |
| Advertisement No. | Advt. No. 81/2026 |
| Name of the Post | Project Scientist-I |
| Project Title | “Quantum Annealing and Optimization: Mobility, Multi-modal logistics, Railway Resource Scheduling and Related Problems” |
| Research Area / Project Code | Quantum Algorithms (Project Code: 30526263) |
| Principal Investigator | Dr. Sambuddha Sanyal |
| No. of Posts | 01 Position |
| Tenure | Temporary for 1 year, extendable up to 3 years based on performance review |
| Upper Age Limit | Not more than 35 years as of the closing date |
| Job Location | IISER Tirupati, Andhra Pradesh, India |
| Application Deadline | October 05, 2026 |
Fellowship & Pay Structure
- Consolidated Pay: ₹77,000/- per month + 9% HRA (upon producing the Ph.D. completion certificate).
Qualification & Experience Criteria
Minimum Qualifications
- Essential Education: Ph.D. degree in Quantum Many-Body Physics (Theory).
- Research Track Record: Demonstrable evidence of high-quality research conducted during or after the Ph.D. program.
Desirable Experience
- Hands-on computational experience in quantum many-body physics (e.g., Exact Diagonalization, Tensor-Network methods, or Quantum Monte Carlo).
- Experience in quantum computing frameworks or classical/quantum optimization techniques.
About Institution
IISER Tirupati is a premier autonomous science education and research institute set up by the Ministry of Education, Govt. of India. It conducts cutting-edge research across fundamental sciences, computational disciplines, and interdisciplinary engineering domains.
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Key Responsibilities
- Algorithm Development: Formulate, test, and benchmark quantum annealing and quantum-inspired algorithms for complex resource allocation and logistics problems.
- Computational Simulations: Utilize tensor networks, exact diagonalization, or quantum Monte Carlo methods to analyze quantum optimization pathways.
- Project & Research Deliverables: Collaborate with the PI on publishing peer-reviewed research and developing solutions for railway and multi-modal logistics scheduling.
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Skills You’ll Gain
| Category | Specific Tools & Competencies Mastered |
| Quantum Computation | Quantum Annealing Formulations, QUBO/Ising Mapping, Variational Quantum Algorithms. |
| Many-Body Physics | Tensor Networks, Exact Diagonalization, Quantum Monte Carlo Simulations. |
| Applied Optimization | Multi-Modal Logistics Scheduling, Railway Network Optimization, Algorithmic Efficiency Analysis. |
Selection Process
- Applications will be reviewed by a Screening Committee.
- Shortlisted candidates will be notified via email for an interview.
- No TA/DA will be admissible for attending the selection process.
Frequently Asked Interview Questions (With Answers & Solution Guidelines)
Quantum Annealing & QUBO Formulations
- Question:“How do you formulate a real-world resource scheduling problem (like railway routing) into a Quadratic Unconstrained Binary Optimization (QUBO) model suitable for quantum annealers?”
- Answer & Solution Context: Map decision variables to binary choices (0 or 1) representing specific time slots, tracks, or train assignments. Construct the objective function to minimize delay or energy while embedding operational constraints (such as track capacity and signal buffer times) as penalty terms within the QUBO matrix.
Tensor Networks vs Quantum Annealing
- Question:“When simulating large-scale quantum optimization problems classically, what advantages do Tensor-Network methods offer over traditional Matrix Product States or Monte Carlo techniques?”
- Answer & Solution Context: Tensor networks (such as MPS and PEPS) efficiently represent low-entanglement quantum states, allowing classical simulation of large system sizes without encountering the sign problem inherent in Monte Carlo methods. They provide insights into ground-state properties and energy gaps during quantum annealing protocols.
How to Apply & Registration Details
- Download the prescribed application form from the official advertisement page.
- Complete the form and convert it into a single PDF document.
- Send the application email to:
- Email Address:
sambuddha.sanyal@labs.iisertirupati.ac.in - Subject Line Format:
Project Scientist-I: 81/2026 - (Note: Sending only a CV without the prescribed application form will lead to rejection).
- Email Address:
- Application Deadline:October 05, 2026
- Official Job Portal Link:View IISER Tirupati Advt 81/2026 Page
- Official Notification PDF:Download Advt 81/2026 PDF