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Computational Science

Computational Science
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Computational Science

Choose a category below to begin. Each category opens into focused areas, and selecting a focused area lets you pick your final internship variant (your preferred track, mode and duration).

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Showing 1–20 of 50

AI High-Performance Computing Research
Internship studying high-performance computing with AI optimisation of scheduling and throughput. Applied sessions reinforce each technique.
5 focused areasClick to view more details →
Machine Learning Simulation Acceleration Research
Internship studying simulation acceleration with ML surrogates that replace costly computation. Interns work with realistic case datasets.
Academic (A)Tech (T)Research (R)
1 Month
A · ₹7,670
T · ₹21,938
R · ₹31,909
3 Months
A · ₹28,847
T · ₹35,257
R · ₹51,283
6 Months
A · ₹64,104
T · ₹78,349
R · ₹1,13,962
14 more durations from 5 days to 1 year
5 focused areasClick to view more details →
AI Physics-Informed Neural Network Research
Internship studying physics-informed neural networks that embed governing equations into learning. Guided practice with real datasets throughout.
5 focused areasClick to view more details →
AI Scientific Data Management Research
Internship studying scientific data management with AI cataloguing, provenance, and reuse support. Includes mentored hands-on analysis sessions.
5 focused areasClick to view more details →
AI Climate Computational Modeling Research
Internship studying climate modelling computationally with AI emulation and downscaling methods. Interns work with realistic case datasets.
5 focused areasClick to view more details →
AI Astrophysics Computational Research
Internship studying computational astrophysics with AI analysis of simulation and survey datasets. Practical exercises anchor every concept taught.
5 focused areasClick to view more details →
AI Exascale Computing Research
Internship studying exascale computing with analysis of scaling, communication, and workload design. Interns practise on genuine research problems.
5 focused areasClick to view more details →
AI Computational Fluid Dynamics Research
Internship studying computational fluid dynamics with AI surrogate models and turbulence closure. Mentor-led sessions build applied skill.
5 focused areasClick to view more details →
AI Digital Twin Scientific Research
Internship studying scientific digital twins that mirror experiments closely enough to guide them. Hands-on work runs alongside theory modules.
5 focused areasClick to view more details →
AI Scientific Workflow Automation Research
Internship studying scientific workflow automation with AI orchestration across computing steps. Practical exercises anchor every concept taught.
5 focused areasClick to view more details →
Quantum Algorithm Implementation Research
Develop and test quantum algorithms for computational problems using simulators and quantum computing frameworks.
5 focused areasClick to view more details →
Molecular Dynamics Simulation Optimization
Optimize molecular dynamics simulations for protein folding and material properties using GPU acceleration.
5 focused areasClick to view more details →
High-Order Numerical Methods Development
Research and implement high-order finite element and spectral methods for partial differential equations.
5 focused areasClick to view more details →
Stochastic Differential Equations Solver Research
Develop numerical solvers for stochastic differential equations with applications in finance and biology.
5 focused areasClick to view more details →
Parallel Computing Architecture Performance Analysis
Analyze performance metrics and bottlenecks in parallel computing systems using profiling tools.
5 focused areasClick to view more details →
Computational Genomics Data Processing
Build pipelines for processing and analyzing large-scale genomic sequences using computational methods.
5 focused areasClick to view more details →
Meshless Methods for Partial Differential Equations
Implement and validate meshless computational methods for solving complex partial differential equations.
5 focused areasClick to view more details →
Uncertainty Quantification in Scientific Computing
Research probabilistic methods for quantifying and propagating uncertainties in computational models.
5 focused areasClick to view more details →
Data Assimilation Techniques Research
Study Kalman filters and variational methods for integrating observational data into simulations.
5 focused areasClick to view more details →
Multi-Scale Simulation Coupling Methods
Develop techniques for coupling simulations across different spatial and temporal scales.
5 focused areasClick to view more details →