Rated Excellent
250+ Courses
30,000+ Learners
95+ Countries
INR ₹0.00
Cart
Sale!
AI/ML for Scientific Discovery Using PyTorch and JAX
Original price was: INR ₹4,999.00.INR ₹2,499.00Current price is: INR ₹2,499.00.
AI/ML for Scientific Discovery Using PyTorch and JAX is a Moderate-level, 3 Days online program by NSTC. Master AI/ML for scientific discovery, PyTorch, and JAX through hands‑on projects, real datasets, and expert mentorship. Earn your e-Certification + e-Marksheet in AI/ML for scientific research. Designed for researchers, scientists, and engineers seeking practical AI expertise in India.
About the Ai/Ml Course
AI/ML for Scientific Discovery Using PyTorch and JAX is a professional training program that introduces learners to artificial intelligence and machine learning techniques for modern scientific research.
You will learn how to leverage AI/ML models for data‑driven discovery, pattern recognition, prediction, simulation, and optimization across domains such as chemistry, materials science, and physics.
Program Highlights
• Comprehensive coverage of AI/ML for Scientific Discovery Using PyTorch and JAX from fundamentals to advanced applications
• Hands-on projects and real-world case studies in AI/ML
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: PyTorch, JAX, Optax, NumPy
• Career-oriented training for academic and professional growth in AI/ML
Course Curriculum
Module 1: Day 1 – Scientific ML Foundations & PyTorch‑Based Property Prediction
- Explore AI/ML roles in scientific discovery and inverse design
- Prepare and visualize scientific datasets (molecular, materials, simulation)
- Implement tensor representations and automatic differentiation in PyTorch
- Design neural networks for property prediction
Module 2: Day 2 – Physics‑Informed Neural Networks & Surrogate Modeling
- Integrate physics‑based loss functions into neural networks
- Encode differential equations, boundary conditions, and conservation laws
- Build surrogate models for expensive simulations
- Perform parameter estimation and inverse modeling
Module 3: Day 3 – JAX for Differentiable Scientific Computing & Optimization
- Utilize JAX transformations (grad, jit, vmap) for high‑performance ML
- Create differentiable scientific computing pipelines
- Optimize parameters with Optax‑based workflows
- Compare PyTorch and JAX for research workloads
Tools, Techniques, or Platforms Covered
PyTorch
JAX
Optax
NumPy
SciPy
JAX
Optax
NumPy
SciPy
Real-World Applications
- Apply AI/ML for Scientific Discovery Using PyTorch and JAX skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical AI/ML competencies
- Solve industry-relevant problems using AI/ML for Scientific Discovery Using PyTorch and JAX methodologies and tools
- Contribute to open-source projects and collaborative research in AI/ML
- Prepare for competitive examinations, interviews, and professional certifications in AI/ML
Who Should Attend & Prerequisites
- Industry‑recognised e‑Certification + e‑Marksheet from NSTC
- Hands‑on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Prerequisites:
Frequently Asked Questions
1. What is the format of this AI/ML for Scientific Discovery Using PyTorch and JAX course?
This is an Recorded Lectures (Self-Paced) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.
2. Will I receive a certificate after completing this course?
Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.
3. What are the prerequisites for this course?
Learners should have a foundational understanding of AI/ML concepts. Familiarity with basic tools and programming is recommended.
4. How long will I have access to the course materials?
You will have access to all course materials for the duration of 3 Days (60-90 Minutes each day). The self-paced format allows you to learn according to your own schedule through our online learning management system.
5. Is mentor support available during the course?
Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to AI/ML. Our mentors are industry experts and experienced professionals.
Enroll in AI/ML for Scientific Discovery Using PyTorch and JAX today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering AI/ML skills that matter.
| Brand | NSTC |
|---|---|
| Format | Recorded Lectures |
| Duration | 3 Days (60-90 Minutes each day) |
| Level | Intermediate |
| Domain | AI/ML |
| Hands-On | Yes – Practical projects with industrial datasets |
| Tools Used | PyTorch, JAX, Optax, NumPy, SciPy |
Related products
-
Sale!
AI for Federated Learning: Decentralized Data & Privacy-Preserving Techniques | NanoSchool
Original price was: INR ₹11,000.00.INR ₹5,499.00Current price is: INR ₹5,499.00. Add to cart -
Sale!
Original price was: INR ₹11,000.00.INR ₹5,499.00Current price is: INR ₹5,499.00. Add to cartAI for Climate Modeling, Extreme Events & Policy Scenario Analysis -
Sale!
AI Model Deployment and Serving
Original price was: INR ₹4,999.00.INR ₹2,499.00Current price is: INR ₹2,499.00. Add to cart -
Sale!
AI for Environmental Sustainability
Original price was: INR ₹4,999.00.INR ₹2,499.00Current price is: INR ₹2,499.00. Add to cart -
Sale!
Generative Adversarial Networks Course
Original price was: INR ₹4,999.00.INR ₹2,499.00Current price is: INR ₹2,499.00. Add to cart
