Artificial Intelligence & Machine Learning Application

Our research institute explores the root causes of global problems and publish reports on critical issues in relation to the UN SDG's. We want to leverage the latest technology to advance our capacity. Applications of this technology include LLM's, RAG, recommendation algorithms, predictive analytics / forecasting like lifetime values, modelling and classifications. We would like to collaborate with students to apply the latest artificial intelligence (AI) and machine learning (ML) techniques to our existing dataset. Students will develop an AI / ML model related to any of the aforementioned applications. This will involve several different steps for the students, including: Conducting background research on our existing products and the dataset. Analyzing our current dataset. Researching the latest AI / ML techniques and how they could be applied to our data. Developing an AI / ML model that provides unique outcomes or insights into our data. Providing multiple solutions that can be applied to solve the same problem.

Literature Summary Research Assistant

Our research institute publishes dozens of papers every year to promote projects in our field of research. Among these papers are systematic reviews of relevant literature, which analyze the progression of research within our field or problems within a specific vertical of the UN SDG's. We would like to collaborate with students to support our literature review process by categorizing papers, summarizing important findings and fact checking. Bonus points if you can develop a software program or workflow that helps execute this at scale. This allows us to extract and process data more easily. This will involve several different steps for the students, including: Familiarizing themselves with our research and the types of academic papers that we are interested in. Reading studies relevant to our area of research. Applying the categorization scheme to classify relevant studies. Summarizing important results from relevant studies. Bonus steps in the process would also include: Working with researchers to draft a publication based on the results.

AI-Enhanced Signal Detection and Analysis System

Earthshot Factory aims to develop a cutting-edge software solution that integrates digital signals detection and analysis with an AI-driven workflow. The primary goal is to create a system capable of identifying and interpreting various signals from digital data sources and analyzing them. This project will allow learners to apply their knowledge of signal processing, machine learning, and software development to build a robust and efficient system. The software should be able to autonomously detect anomalies, classify different types of signals, and provide actionable insights through an intuitive user interface. The project will involve designing algorithms for signal detection, training AI models for analysis, and integrating these components into a cohesive workflow. The output should be a full-stack web application that allows the user to approve or reject the execution of the AI agent's recommended action.

Digital Networking Enhancer

In today's digital age, building and maintaining professional connections is crucial for career growth and business development. However, many existing platforms either focus on quantity over quality or lack the tools to foster meaningful relationships. Earthshot Factory aims to address this gap by developing a full-stack application prototype that enhances both the quality and quantity of professional connections. The goal is to create a user-friendly platform that leverages advanced algorithms and user data to recommend valuable connections, facilitate meaningful interactions, and provide analytics to track relationship growth. This project will allow learners to apply their knowledge in full-stack development, user experience design, and data analytics to solve a real-world problem.