Opportunities for Co-Op and Industry-PhD Projects

Axiomatic AI"s mission :

Axiomatic AI is launching with the aim to accelerate R&D by "Automated Interpretable Reasoning" (AIR) a verifiably truthful AI model built for reasoning in science and engineering.

Axiomatic AI is hiring top talent interested in a future of human reasoning aided by not replaced by AI, and a future that empowers a new generation of innovators to solve important problems through deep-tech engineering in the semiconductor ecosystem.

Please see below for co-working project opportunities at Axiomatic AI.

Competitive Programming Projects

Project 1 : Enhancing AI-Powered Code Synthesis

Overview : This project focuses on advancing the capabilities of AI-powered code synthesis tools like AlphaCode. The goal is to develop algorithms that can automatically generate efficient and correct code from high-level problem descriptions.

• Objectives :
• Develop new algorithms for code generation that improve upon current state-of-the-art models.
• Implement a robust verification system to ensure the correctness of the generated code.
• Integrate the system with Axiomatic AI's CDT generator and verifier.
• Expected Outcomes :
• Enhanced code synthesis capabilities.
• Improved accuracy and efficiency in generated code.
• Seamless integration with Axiomatic AI's existing platforms.
• Requirements : Strong background in machine learning, natural language processing, and programming languages.
• References :

AlphaCode : Developing Code Generation Algorithms" Research Paper

GitHub - AlphaCode Repository , Codex Examples

Project 2 : Optimizing Code through Automated Refactoring

Overview : This project aims to develop AI-driven tools for automated code refactoring, improving code quality and maintainability.

The focus is on integrating these tools with Axiomatic AI's suite of optimizers.

• Objectives :
• Create algorithms for identifying refactoring opportunities in codebases.
• Develop methods for automated code refactoring and optimization.
• Test and validate the tools within real-world code repositories.
• Expected Outcomes :
• Automated tools for code refactoring.
• Improved code quality and performance.
• Integration with Axiomatic AI's optimization suite.
• Requirements : Experience in software engineering, machine learning, and software optimization techniques.
• References :
• AlphaProof
• SlotFormer : Unsupervised Visual Dynamics Simulation with Object-Centric Models : Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf, and Animesh Garg arXiv preprint arXiv : 2210.05861 2022

Project : AI Code Synthesis for Microelectronics Design

Overview : This project focuses on developing AI-driven code synthesis tools for automating the design and verification of microelectronic circuits.

• Objectives :
• Develop AI algorithms for generating Verilog / VHDL code for microelectronics designs.
• Implement a verification system to ensure the correctness of synthesized designs.
• Test and validate the system on real-world microelectronics projects.
• Expected Outcomes :
• Automated code synthesis tools for microelectronics design.
• Improved design efficiency and correctness.
• Validation on real-world microelectronics projects.
• Requirements : Strong background in digital circuit design, Verilog / VHDL, and machine learning.
• References :

Micro / Nano Circuits and Systems Design and Design Automation" Research Paper

• GitHub - OpenROAD : Open Source EDA
• https : / / arxiv.org / abs / 2405.16380
• GitHub - EDA Tools and Resources
• https : / / ieeexplore.ieee.org / document / 10253952

Project : AI-Driven Photonic Integrated Circuit (PIC) Design Automation

Overview : This project aims to create AI-powered tools for the design and optimization of photonic integrated circuits (PICs), enhancing the design process and reducing time-to-market.

• Objectives :
• Develop AI algorithms for synthesizing PIC designs from high-level specifications.
• Create optimization techniques for improving PIC performance and efficiency.
• Validate the tools with real-world PIC designs.
• Expected Outcomes :
• AI-driven synthesis tools for PIC design.
• Enhanced performance and efficiency of PICs.
• Successful validation with real-world PIC projects.
• Requirements : Expertise in photonic circuit design, optimization algorithms, and machine learning.
• References :
• https : / / proceedings.mlr.press / v235 / chen24ad.html
• GitHub - Photonics Simulation Tools

Digital Twins Projects

Project 1 : Advanced Digital Twin Integration for AXI

Overview : This project explores the integration of digital twin technologies within the Axiomatic AI framework, focusing on real-time data synchronization and predictive analytics.

• Objectives :
• Develop methods for real-time data integration from IoT devices into digital twins.
• Implement predictive analytics to enhance operational efficiency.
• Validate the system in AXI-relevant industries.
• Expected Outcomes :
• Real-time integrated digital twin systems.
• Enhanced predictive analytics capabilities.
• Demonstrated benefits in AXI-relevant industries.
• Requirements : Background in IoT, data analytics, and digital twin technologies.
• References :

Digital Twin for Industry 4.0 : Real-Time Integration and Analytics" Research Paper

GitHub - Azure Digital Twins

Predictive Analytics in Industry 4.0 Using Digital Twins" Research Paper

GitHub - Industry 4.0 Solutions

Project 2 : Digital Twin Framework for Engineering Systems

Overview : This project focuses on creating a comprehensive digital twin framework for engineering systems, enabling better design, simulation, and validation processes.

• Objectives :
• Develop a scalable framework for creating digital twins of engineering systems.
• Integrate real-time data from various engineering processes.
• Implement analytics for design and operational optimization.
• Expected Outcomes :
• Scalable digital twin framework for engineering systems.
• Enhanced design and operational optimization capabilities.
• Successful pilot deployment in AXI-relevant engineering projects.
• Requirements : Expertise in engineering design, data integration, and digital twin technologies.
• References :
• https : / / nap.nationalacademies.org / catalog / 26894 / foundational-research-gaps-and-future-directions-for-digital-twins?

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NVIDIA Omniverse

Probabilistic Machine Learning Projects

Project 1 : Probabilistic Models for Uncertainty Quantification in AI

Overview : This project aims to develop probabilistic models that can quantify uncertainty in AI predictions, improving the reliability of AI systems.

• Objectives :
• Develop new probabilistic models for uncertainty quantification.
• Integrate these models with existing AI systems to enhance decision-making.
• Validate the models in real-world applications.
• Expected Outcomes :
• Improved uncertainty quantification models.
• Enhanced reliability of AI predictions.
• Successful integration and validation in real-world scenarios.
• Requirements : Strong background in probabilistic modeling, statistics, and machine learning.
• References :
• https : / / probml.github.io / pml-book / book1.html
• GitHub - Bayesian Deep Learning

Project 2 : Integrating Factor Networks and Knowledge Graphs for Enhanced AI Reasoning

Overview : This project explores the use of factor networks and knowledge graphs to improve AI reasoning and decision-making processes.

• Objectives :
• Develop methods for integrating factor networks with knowledge graphs to represent complex relationships.
• Apply these integrated models to enhance AI reasoning and inference capabilities.
• Validate the effectiveness of the integrated models in real-world scenarios.
• Expected Outcomes :
• Advanced techniques for integrating factor networks and knowledge graphs.
• Improved AI reasoning and decision-making capabilities.
• Validation through case studies in various domains.
• Requirements : Expertise in probabilistic graphical models, knowledge graphs, and machine learning.
• References :

Knowledge Graphs : Principles and Applications" Research Paper

GitHub - Knowledge Graph Toolkit

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