For Data Scientists, PhD Students & Advanced ML Engineers

Master Physics-Based AI: The 16-Week R&D Intensive

Transition from a standard ML practitioner to an elite AI architect. Learn to embed physical laws into your architectures to build highly predictable models with a fraction of the data — and gain the deep theoretical intuition that automated AI coding agents simply cannot replicate.

The Era of "Glue-Code" ML Is Ending

You already know the daily frustrations of conventional, data-hungry machine learning. But there is a bigger, existential problem: standard ML pipelines are being automated. If your primary skill is stitching together pre-built architectures and feeding them data, your role is becoming a commodity.

• The Data Bottleneck: Conventional models demand massive, expensive, densely-labeled datasets that often don't exist for niche scientific or engineering problems.
• The Generalization Trap: Standard architectures memorize rather than understand, failing to generalize outside their narrow training distribution.
• The Black Box Problem: Models operate blindly, frequently violating fundamental laws, logic, and safety constraints once deployed in the real world.
• The Automation Threat: Codex, Claude Code, and automated ML pipelines already write boilerplate PyTorch and tune hyperparameters — commoditizing the API-wrapper role.

The Transformation

This 16-week intensive transitions you from a consumer of standard ML into an architect of next-generation, physics-based AI. You'll gain the deep theoretical intuition required to debug complex systems, break down arXiv papers, and design novel architectures from scratch. By the end, you will be able to:

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• Shatter the Data Bottleneck: Train accurate, generalizable models with a fraction of the data by leveraging physics principles and symmetries (Equivariant Neural Networks).
• Open the Black Box: Eliminate unpredictable model behavior by embedding strict physical constraints and differential equations directly into your architectures (PINNs & Hamiltonian Neural Networks).
• Architect, Don't Assemble: Build bespoke, next-generation generative models (Energy-Based Models & Continuous Normalizing Flows) from scratch instead of relying on off-the-shelf APIs.
• Future-Proof Your Career: Secure your position as a high-value, specialized R&D Data Scientist whose theoretical depth and architectural skill cannot be replaced by automated coding agents.

Your Instructor: Ardavan Borzou, PhD

Led by Ardavan Borzou, PhD, Co-Founder of CompuFlair. Very few ML instructors have operated at the absolute extremes of physics and data. Ardavan holds a dual PhD in Physics, specializing in experimental high-energy physics at the LHC's CMS experiment and in theoretical gravity.

At the LHC, he spent years building statistical models to extract meaningful, microscopic structure from overwhelming noise — the same mathematical foundation that governs modern machine learning. Following an award-winning, Springer-published dissertation and a Data Science Fellowship at the National Library of Medicine, he now focuses entirely on the intersection of physics and AI.

Dual PhD

Physics & Gravity

LHC · CMS

Experimental HEP

Springer

Author

The Curriculum

16 Weeks · 7 Core Architectures · Concepts, Scratch & PyTorch

Module 01

Energy-Based Models (EBMs)

The unifying framework for most ML models. Unlocks extreme flexibility for structured prediction by modeling data via energy landscapes rather than rigid, normalized probabilities.

Module 02

Equivariant Neural Networks (ENNs)

Eliminate brute-force data augmentation. Hard-code geometric symmetries (rotation, translation, etc.) directly into your architecture, so your model learns physical invariants instantly — saving massive amounts of training data and compute.

Module 03

Continuous Normalizing Flows (CNFs)

Break free from rigid, discrete layers. Use continuous-time differential equations to build highly adaptive, perfectly invertible generative models that map complex distributions far more elegantly than standard blocky architectures.

Module 04

Physics-Informed Neural Nets (PINNs)

Train accurately even when data is scarce. Use differential equations to penalize physical-law violations during training, forcing the model to learn the underlying reality rather than just memorizing noise.

Module 05

Hamiltonian Neural Networks (HNNs)

Prevent long-term prediction drift. Standard ML models degrade rapidly when simulating dynamics. HNNs learn the exact underlying energy function, preserving strict conservation laws and guaranteeing long-term stability where conventional ML collapses.

Module 06

Diffusion Models

Master the engine of modern generative AI. Move beyond the instability of traditional GANs. Leverage highly stable, likelihood-based optimization to reverse noise processes, giving you mathematically sound control over complex generative tasks.

Module 07

Next-Gen Generative AI

Stay ahead of the automation curve. Look past what AI coding agents can currently build and explore the absolute bleeding edge of physics-based generative models, so you are positioned to lead the next major paradigm shift in the industry.

The Senior ML Engineer

Frustrated by the massive data requirements and black-box unpredictability of standard architectures, you want to build more robust, predictable systems.

The STEM Academic / Postdoc

You have a deep background in physics, math, or engineering, and you want to translate that theoretical intuition into highly lucrative AI architecture skills.

The Future AI Architect

You want to understand and build the foundational models of tomorrow — not just write prompt wrappers for the models of today.

The Founding Cohort: An Exclusive Live R&D Experience

We are opening the doors to our inaugural Founding Cohort. Join now and you get a rigorous curriculum plus unprecedented, direct access to the CompuFlair team to dissect complex models in real time. Because this is the first iteration, this high-touch guidance is strictly limited and will never be offered at this price or format again.

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• Live Architecture Tear-Downs: Every week, dissect complex mathematical frameworks with Ardavan live, debug your PyTorch, and ask your toughest theoretical questions.
• Direct Curriculum Influence: As a founding member, your feedback shapes the course — we adapt live sessions to dive deeper and solve your specific roadblocks.
• The Immediate Replay Vault: Every live session is recorded and uploaded immediately, giving you complete flexibility around an unpredictable schedule.
• Lifetime Access: Keep lifetime access to the finalized, highly-polished course once Phase 2 launches.

The Investment

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Frequently Asked Questions

Everything you need to know before joining the Founding Cohort

Q1

How much time will I need each week?

Expect 4–6 hours per week. That includes the 1.5-hour live session plus time to work through the scratch and PyTorch implementations.

Q2

My ML is strong, but my physics is rusty. Will I fall behind?

Not at all. If you're comfortable with college-level calculus, linear algebra, and probability, you're ready. We teach the physical concepts from the ground up before translating them into code.

Q3

What if I can't attend the live sessions?

No problem. Every live session is recorded and uploaded immediately to your Replay Vault. You can submit questions ahead of time and Ardavan answers them on the recording.

Q4

Do I need a massive GPU cluster?

Absolutely not. The entire premise of Physics-Based AI is data and compute efficiency. You'll build highly predictable models that don't require supercomputers — standard cloud notebooks like Google Colab Pro are perfectly fine.

Q5

Will this course be offered again later?

Yes, but not in this format or at this price. In Phase 2 it becomes a self-paced, evergreen program. The live access to Ardavan, direct curriculum influence, and weekly debugging sessions are reserved for this Founding Cohort.

Q6

What are the technical prerequisites?

You should bring fluency in Python (clean, functional code), comfort with calculus and linear algebra, and a working understanding of basic neural networks and ML fundamentals.

from scratch