Computer software for visualization and implementation of engineering analysis of structure usingArtificial Intelligence & Machine Learning Application

Computer software for visualization and implementation of engineering analysis of structure against applied loads, strain, cracks, tilt measurement, and corrosion Our company is a structure health monitoring company that provides services to many different types of structure via health monitoring systems wirelessly sensors installed,analyzed and reported to the stakeholders. We want to leverage the latest technology in the market to create advantage for our stakeholders so they can take decisions real time with real time data. Applications of this technology include recommendation algorithms, and predictive analytics like lifetime value and classifications of the infrasturcture data.  About the project : It is an web app computer software for visualization and implementation of engineering analysis of structure against applied loads, strain, cracks, tilt measurement, and corrosion. The tool allows the client to visually understand the measuring variables causing any long-term or short-term effect on the structural integrity. The downloadable user-friendly reports allows the stakeholders to make informed decisions based on the real-time data input from sensors being directed on the portal.  We would like to collaborate with students in their Grad year, Master or PhD students to apply the latest engineering research in their field of experience computer systems, civil engineering or electrical engineering. They may also add their experience using artificial intelligence (AI) and machine learning (ML) techniques to our existing dataset. Students could also develop an AI / ML model related to  engineering analysis of structure against applied loads, strain, cracks, tilt measurement, and corrosion The Grad year, Master or PhD student will get to work with real-time problems and real-time data to understand the behaviour of structures. They will also learn and understand the practical use and delivery methods of the data for the stakeholders. The student will also get to work with 5-6 different types of sensor data and help classify and filtrate for the most optimal visualization of the data and its representation to the stakeholder/client. 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.(Optional )  Providing multiple solutions that can be applied to solve the same problem. Working alongside a team consisting of - a product manager, and computer software/developers.  Get access to and first-hand experience in different types of structural sensor data  Consider introducing different software or analytical methods that can be added to the platform. Classification and filtration of infrastructure data coming from 6-7 different type of sensors

Technical Forum Engagement and Content Strategy

Progressive Automations, Inc seeks to enhance its brand presence and gather valuable insights by engaging with technical forums. The project aims to address user questions, share relevant articles, and collect data for content development and competitive analysis. By participating in these forums, the company can better understand customer needs, identify industry trends, and improve its content strategy. The project will involve researching popular technical forums related to automation, drafting responses to user inquiries, and sharing Progressive Automations' articles to establish authority. Additionally, learners will analyze forum discussions to gather insights that can inform future content creation and competitive positioning. - Research and identify key technical forums related to automation. - Engage with users by answering questions and sharing relevant content. - Gather insights from forum discussions for content development. - Analyze competitor activity within these forums.

Agora Tutoring - Platform: Manual QA Testing for Enhanced User Experience

Project Objective The primary goal is to perform comprehensive manual testing on Agora Tutoring's platform to identify bugs, usability issues, and workflow inefficiencies. By testing every functionality and exploring various user pathways, this project aims to ensure the platform is robust, user-friendly, and ready to support its growing user base.

IT Analyst Team for a Data analytics infrastructure Software

IT Analysts as Product Managers: Goals and Responsibilities Project Goal To oversee the successful development and delivery of the Civioniks platform, a web-based application for structural health monitoring. The IT Analysts, acting as Product Managers, will ensure the platform meets the needs of stakeholders by enabling real-time data analysis, visualization, and reporting of structural variables such as applied loads, strain, cracks, tilt, and corrosion. Their role is to align the platform’s functionality with user requirements, deliver value-driven outcomes, and manage cross-functional collaboration effectively. Key Responsibilities 1. Product Vision and Strategy Define the product vision to align with the project’s objectives and stakeholder needs. Develop a product roadmap that outlines major milestones, deliverables, and timelines. Ensure that the platform’s development focuses on user-centric functionality, including visualization tools and actionable reports. Deliverables: Product vision statement. A detailed roadmap with phases for development, testing, and deployment. 2. Stakeholder Engagement Act as the primary liaison between stakeholders and the development team. Gather requirements through meetings, interviews, and workshops with stakeholders. Translate stakeholder feedback into actionable features and prioritize them for development. Deliverables: Stakeholder requirements document. Prioritized feature backlog. 3. Requirement Gathering and Documentation Collaborate with developers, data analysts, and engineers to understand technical and functional needs. Create user stories , workflows, and process diagrams that reflect the platform’s objectives. Define acceptance criteria for each feature. Deliverables: Comprehensive Requirements Specification Document. Use case diagrams and workflows. 4. Backlog Management Maintain a product backlog, ensuring tasks are well-defined, prioritized, and aligned with project goals. Work closely with the team to plan and execute development sprints. Regularly review and refine the backlog based on feedback and progress. Deliverables: Updated and prioritized product backlog. Sprint planning documentation. 5. Cross-Functional Collaboration Coordinate efforts between the UI/UX, backend, and data analytics teams to ensure seamless integration. Organize and facilitate sprint reviews, retrospectives, and daily stand-ups. Address bottlenecks or blockers during development. Deliverables: Collaboration meeting minutes and action items. Progress tracking reports. 6. Product Development Oversight Oversee the development of: Visualization tools for engineering data (e.g., strain, cracks, tilt). Real-time analytics and reporting features . Backend systems for processing and storing sensor data. Ensure deliverables meet agreed-upon acceptance criteria. Deliverables: Functional MVP (Minimum Viable Product) of the platform. Feature-specific tracking and testing reports. 7. User Experience (UX) and Interface Design Collaborate with UI/UX designers to ensure the platform is intuitive and user-friendly. Validate visualizations and dashboards with stakeholders to meet usability standards. Test the platform for accessibility and responsiveness. Deliverables: Wireframes and UI mockups. Feedback reports from usability testing. 8. Testing and Quality Assurance Define success metrics and KPIs for the platform. Oversee system testing to ensure: Accuracy of real-time data. Functionality of visualization and reporting tools. Stability and scalability of the platform. Collaborate with the QA team to address any issues. Deliverables: QA test plans and bug-tracking reports. Final approval for deployment readiness. 9. Deployment and Post-Launch Plan and execute the deployment of the platform in a production environment. Oversee initial user training and documentation handover. Gather post-launch feedback and define future updates or improvements. Deliverables: Deployed platform. Training materials (manuals, video tutorials). Post-launch feedback report and roadmap for subsequent versions.