GTDT — Systems Engineering (SCIV_JD-01)

Scivision Center of Excellence Private Limited; Bengaluru; In loco; INR 900.000 - 1.300.000; Tempo pieno

Descrizione del lavoro

SciVision Center of Excellence Pvt. Ltd. invites engineers to lead the development of a physics‑based, AI‑enabled Gas Turbine Digital Twin (GTDT) on the IISc campus, Bengaluru.

You will architect the twin, integrate thermodynamics, combustion, CFD, structural and AI models, and guide a multidisciplinary team to deliver validated, decision‑ready insights. A strong foundation in Brayton‑cycle fundamentals is expected.

Competenze

• 0–3 years in gas turbine, propulsion or rotating‑machinery engineering.
• Strong Brayton-cycle and component‑matching fundamentals.
• Evidence of integrating models, simulations or test data across disciplines.
• Python or MATLAB; ability to review engineering code and 3D models.
• A record of leading people and learning new methods.

Mansioni

• Own the twin architecture, requirements, interfaces and model‑fidelity strategy.
• Integrate thermodynamic, combustion, 3D CFD, 3D structural, controls and AI models into one validated system.
• Lead verification, validation and uncertainty quantification.
• Formulate optimisation and maintenance decisions on the twin.
• Mentor engineers and chair fortnightly technical reviews.

Conoscenze

Python

MATLAB

Leadership

Multidisciplinary design

Model integration

Formazione

M.Tech in Aerospace/Mechanical/ Thermal or Propulsion

Ph.D. welcome

Descrizione del lavoro

SciVision Center of Excellence Pvt. Ltd. · GTDT: Deep-Tech Engineering

M.Tech. (Aerospace, Mechanical, Thermal or Propulsion); Ph.D. welcome

About Us

SciVision Center of Excellence Pvt. Ltd. is a deep-tech engineering R&D company on the IISc

campus, Bengaluru. We are building a physics-based, AI-enabled digital twin of a gas turbine —

thermodynamics, combustion, 3D CFD and structural analysis, engine controls, life prediction,

physics-informed AI and multidisciplinary optimisation.

The Role

Lead the architecture, integration and validation of a physics-based, AI-enabled Gas Turbine Digital

Twin (GTDT) and build a multidisciplinary team that learns fast and applies what it learns.

What You Will Do

• Own the twin architecture, requirements, interfaces and model-fidelity strategy.
• Integrate thermodynamic, combustion, 3D CFD, 3D structural, controls and AI models into one
  validated system.
• Lead verification, validation and uncertainty quantification.
• Formulate optimisation and maintenance decisions on the twin.
• Mentor engineers and chair fortnightly technical reviews.

What You Bring

• 0-3 years in gas turbine, propulsion or rotating-machinery engineering.
• Strong Brayton-cycle and component-matching fundamentals.
• Evidence of integrating models, simulations or test data across disciplines.
• Python or MATLAB; ability to review engineering code and 3D models.
• A record of leading people and learning new methods.

What You Will Learn

Structured training in digital twins and multidisciplinary design optimisation, with protected learning

time and mentoring. Lead the team’s multidisciplinary optimisation and decision-under-uncertainty

work on a live twin.

How We Hire

We hire engineers who learn fast and apply what they learn. Selection includes a technical interview,

a short supervised Learn-and-Apply exercise and a practical assessment.

SciVision selects on competence and evidence of work. We do not discriminate on age, gender, caste, religion, disability or any other protected characteristic.