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:
• 1. Develop new algorithms for code generation that improve upon current state-of-the-art models.
  2. Implement a robust verification system to ensure the correctness of the generated code.
  3. 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:
• 1. "AlphaCode: Developing Code Generation Algorithms" Research Paper
  2. 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:
• 1. Create algorithms for identifying refactoring opportunities in codebases.
  2. Develop methods for automated code refactoring and optimization.
  3. 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:
• 1. AlphaProof
  2. 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:
• 1. Develop AI algorithms for generating Verilog/VHDL code for microelectronics designs.
  2. Implement a verification system to ensure the correctness of synthesized designs.
  3. 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:
• 1. "Micro/Nano Circuits and Systems Design and Design Automation" Research Paper
  2. GitHub - OpenROAD: Open Source EDA
  3. https://arxiv.org/abs/2405.16380
  4. GitHub - EDA Tools and Resources
  5. 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:
• 1. Develop AI algorithms for synthesizing PIC designs from high-level specifications.
  2. Create optimization techniques for improving PIC performance and efficiency.
  3. 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:
• 1. https://proceedings.mlr.press/v235/chen24ad.html
  2. 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:
• 1. Develop methods for real-time data integration from IoT devices into digital twins.
  2. Implement predictive analytics to enhance operational efficiency.
  3. 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:
• 1. "Digital Twin for Industry 4.0: Real-Time Integration and Analytics" Research Paper
  2. GitHub - Azure Digital Twins
  3. "Predictive Analytics in Industry 4.0 Using Digital Twins" Research Paper
  4. 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:
• 1. Develop a scalable framework for creating digital twins of engineering systems.
  2. Integrate real-time data from various engineering processes.
  3. 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:
• 1. https://nap.nationalacademies.org/catalog/26894/foundational-research-gaps-and-future-directions-for-digital-twins?utm_source=NASEM+Math+and+Statistics&utm_campaign=87b2f564c2-EMAIL_CAMPAIGN_2023_05_15_01_42_COPY_01&utm_medium=email&utm_term=0_-a0739a5cef-%5BLIST_EMAIL_ID%5D
  2. 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:
• 1. Develop new probabilistic models for uncertainty quantification.
  2. Integrate these models with existing AI systems to enhance decision-making.
  3. 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:
• 1. https://probml.github.io/pml-book/book1.html
  2. 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:
• 1. Develop methods for integrating factor networks with knowledge graphs to represent complex relationships.
  2. Apply these integrated models to enhance AI reasoning and inference capabilities.
  3. 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:
• 1. "Knowledge Graphs: Principles and Applications" Research Paper
  2. GitHub - Knowledge Graph Toolkit