Interactive Project Gantt Chart with Resource Utilization

Background Traxidy project management work tracking software has been available for about two years and is continually improving. Our goal is to be an easy-to-use, intuitive application for project managers to use to control project tasks, report status, and collaborate with teammates. The ability of Project Managers to plan and monitor project tasks and team member utilization, using a detailed Gantt chart will help to properly plan and control the project.   Work Requirements Work directly with the Traxidy CEO and Founder to build an easy-to-use, stand-alone Gantt chart (that would ultimately be integrated into the Traxidy application at a later time) that will: Align the information used in the stand-alone Gantt with our existing application task/action data sets. Manage tasks/actions in a bi-directional manner Create a team member utilization assessment See our Traxidy app at https://traxidy.com and a brief overview HERE . The following links show examples of concepts we like; DHX and Bryntum

Data Management Intern at COOCO

COOCO is an AI-powered platform dedicated to reducing food waste and promoting sustainability by helping users manage their kitchen smarter. One key challenge we face is ensuring users make the most of their ingredients before they expire. Our goal for this project is to collaborate with interns to develop an AI-driven solution focused on optimizing ingredient tracking . Specifically, we want to leverage Artificial Intelligence (AI) and Machine Learning (ML) to predict which ingredients are at risk of expiring soon and suggest actionable ways to use them, such as personalized recipe recommendations. This project offers learners a focused opportunity to apply AI/ML techniques to a real-world problem while contributing to COOCO’s mission of sustainability. Interns will work with our existing dataset—comprising user ingredient inventories and expiration dates—to build a predictive model that enhances our platform’s ability to minimize food waste. The project involves the following steps: Exploring COOCO’s ingredient tracking dataset and its structure. Researching AI/ML techniques suitable for predicting expiration risks. Developing a simple AI/ML model to identify at-risk ingredients and suggest uses. Testing the model and refining it based on results. How will you support learners in completing the project? COOCO is committed to providing a supportive learning environment. Interns will have direct access to our team for mentorship and guidance, including: An introduction to our ingredient tracking system and dataset, with clear documentation. Answers to questions about the data or platform features. Feedback on model development and suggestions for refining approaches. Access to industry insights on how AI/ML is applied to food management (no advanced expertise required). We will also provide a sample dataset upfront and regular check-ins to ensure learners stay on track and feel confident in their progress. Why this project is great for learners This internship is designed to bridge classroom knowledge with practical application. You’ll gain hands-on experience in: Working with real-world data to solve a meaningful problem. Applying AI/ML techniques in a focused, manageable way. Contributing to a sustainability-focused mission that impacts everyday life. Rather than tackling a broad scope, you’ll dive deep into one specific challenge—optimizing ingredient tracking—making this a rewarding and achievable learning experience.