Invoice Processing Projects

STAR-CCM+ + TAITherm Cabin HVAC Thermal Comfort Debug/Fix

Automotive Cabin Heating Thermal Comfort Simulation Fix (STAR-CCM+ .sim + TAITherm Human Thermal / Berkeley Comfort) We are looking for an engineer who can run and troubleshoot an automotive cabin heating thermal comfort simulation using: - Simcenter STAR-CCM+ (.sim) for cabin airflow/temperature fields - TAITherm Human Thermal / HTE + Berkeley Comfort for occupant comfort outputs Task goal (what you need to deliver) We have a simplified cabin model. Under a cold ambient heating scenario, the current results are abnormal: occupant thermal sensation/comfort improves briefly and then becomes colder again (e.g., TS drops toward -1 and continues decreasing). We need you to diagnose the root cause and correct the model setup/coupling/mapping so the outputs are physically reasonable. Target requirements (high level): - Transient duration: 1800 s (30 min) - Monitoring temperatures: foot/foot-vent zone around ~35°C, head point around ~20°C (exact monitor definitions will be shared privately) - Comfort trend: overall thermal sensation & comfort must be normal/stable (after heating ramps up, it should not “get colder again”) What we expect from you - Identify why comfort trend becomes colder (typical causes include MRT/radiation boundary, incorrect field-to-human mapping of Ta/Tr/velocity/RH, initialization/restart issues, boundary leakage on cut surfaces, etc.) - Provide corrected settings + clear explanation of what was wrong and what you changed - Provide plots/outputs for monitoring temperatures + comfort metrics (TS/PMV/PPD or equivalent) Required experience (please do NOT apply if you don’t have this) - Hands-on experience with STAR-CCM+ transient cabin HVAC/thermal - Hands-on experience with TAITherm Human Thermal / Berkeley Comfort (not only CFD) - Ability to actually run the case (access to software/licenses or a workable setup) Confidentiality Due to confidentiality, we will share the detailed model files and full requirement document after you confirm capability. Please answer these in your proposal: 1) Have you used TAITherm Human Thermal / HTE + Berkeley Comfort in real projects? Share 1–2 lines of what you did (no confidential details). 2) Do you have access to STAR-CCM+ and TAITherm comfort/human modules to run the model? (Yes/No) 3) List your top 3 likely causes when cabin heating comfort “gets colder again” (keywords are fine: MRT/radiation, mapping, restart/initialization, boundary condition, cut-surface leakage, etc.) 4) What is your estimated time (days) to deliver corrected results + explanation? 5）Tell me how much you would charge for my task. —————————— Keywords: STAR-CCM+ .sim, Simcenter, cabin HVAC heating, automotive thermal management, TAITherm 2020, ThermAnalytics, Human Thermal Model, Human Modeling Extension (HTE), Berkeley Comfort Model, thermal sensation (TS), PMV, PPD, MRT (mean radiant temperature), radiation boundary, coupling/mapping, transient 1800s, monitoring points, HDF5 .tdf, .vfs .slf .tlf - Simcenter STAR-CCM+ (Transient / CHT / Radiation) - TAITherm / RadTherm (Human Thermal / HTE) - Thermal Comfort / Berkeley Comfort / PMV-PPD - Automotive HVAC / Cabin Thermal Management - CFD Post-processing / Data analysis (HDF5)

opportunity

Biomedical App Service Engineer diagnostic Troubleshoot web/app

We are developing a web-based and mobile biomedical service assistant designed to help biomedical engineers, technicians, and service professionals troubleshoot medical equipment issues efficiently. The application will combine: A guided UI (equipment selection, issue type, image upload) AI-powered troubleshooting A private knowledge repository (service manuals, PDFs, internal documents) Support for multiple AI providers and API keys The goal is to reduce troubleshooting time, standardize service workflows, and make expert-level guidance available instantly. Attached(RFP Doc) Executive RFP Summary Biomedical Service AI Assistant (Web & Mobile) Project Overview We are seeking a qualified software development partner to build a Biomedical Service AI Assistant, a web-based and mobile application designed to help biomedical engineers and service technicians troubleshoot medical equipment efficiently. The platform will combine guided user input, AI-powered diagnostics, image analysis, and a private repository of service manuals, delivering fast, OEM-aware troubleshooting support in both online and offline environments. Key Objectives Reduce medical equipment downtime Standardize biomedical service workflows Provide instant, AI-assisted troubleshooting Prioritize internal service manuals over public AI knowledge Support multilingual output for global service teams Core Features Guided Equipment Selection Users select: Equipment type (Ultrasound, CT, MRI, etc.) Manufacturer and model (e.g., GE LOGIQ E9) Software / firmware version The system auto-generates a structured troubleshooting prompt and prevents redundant questions. AI-Powered Diagnostics (Multi-Provider) Supports local AI (offline, no cost) and cloud AI providers Modular architecture with multiple API keys per provider Automatic fallback when quotas are exceeded No hard-coded keys; all managed via an admin interface Private Knowledge Repository Upload and manage OEM service manuals (PDF format) AI searches internal documentation first (RAG approach) Reduces hallucinations and improves technical accuracy Image-Based Troubleshooting Upload photos of error screens, artifacts, probes, connectors AI analyzes images together with equipment context Always processes the most recent upload Multilingual Translation (Output Level) Translate AI responses from English into: Spanish, Portuguese, French, Italian, German Toggle translation per response Preserves technical terminology and safety warnings Platforms Web application (desktop and tablet) Mobile application (iOS & Android or cross-platform) Shared backend and AI logic across platforms Administration & Configuration Manage AI providers and API keys Enable/disable local or cloud AI engines Upload and organize service manuals Configure supported translation languages Target Users Biomedical engineers Imaging service technicians Independent service providers Hospitals and imaging centers Deliverables Production-ready web application Mobile application Secure backend API Admin configuration panel Scalable AI and document-search architecture Ideal Development Partner Experience with web and mobile applications Proven AI / LLM integration expertise Familiarity with document search or RAG systems Healthcare or technical service experience preferred One-Line Summary A smart biomedical service assistant that combines AI, service manuals, and guided workflows to deliver fast, multilingual, OEM-aware troubleshooting on web and mobile platforms.

Short‑Form Video Editor for UGC‑Style Service Ads

Create 30–60s vertical testimonial‑style videos for local trade businesses (starting with roofers). Overview: I’m testing a new service for local SMEs in the trades: turning 4–5‑star customer reviews into short, cinematic videos for websites and social media. I need a low‑cost video editor to turn my detailed prompts and reference files into visually strong, UGC‑style vertical videos. What you will create: 45–60s vertical 9:16 videos (TikTok/Reels/Shorts). Based on time‑coded prompts I provide (e.g. 00:00–00:05 before shot, 00:05–00:20 arrival, 00:20–00:40 transformation, 00:40–00:60 CTA). “Show, don’t tell” service fulfilment: before/after, close‑ups of tools, hands, materials, process shots, movement. On‑screen text overlays for key ideas and CTA (e.g. “TURNED UP & COMPLETED”, “5 STARS”, “REPLY ‘ROOF’ FOR FREE QUOTE”). Simple sound design: background music and adding supplied or AI voiceover. What I will provide: A written prompt for each video, including structure, scenes, transitions and CTA. The review text (voiceover + optional on‑screen text). The business website for logo/brand reference. A Trade_Video_Reference_Library sample video file (style and pacing). A Video Production Quality Control Checklist (clear pass/fail criteria). Knowledge‑base/reference files where needed. Your responsibilities: Use stock, AI clips or supplied visuals to match each prompt as closely as possible. Follow the QC checklist, including clear before/after contrast and strong visual storytelling. Add basic motion graphics when requested (e.g. stars, impact dust, CTA frame). Deliver MP4 optimised for Reels/TikTok/Shorts, plus project files if possible. Style & quality: UGC‑style authenticity: slightly imperfect, gritty, real‑world feel suited to tradespeople. Still needs to look intentional and clear: clean framing, readable text, logical flow. Strong emphasis on: Before/after transformation. Hands, tools, textures, surfaces. Movement, time‑lapse, transitions. A strong final CTA frame. Initial deliverables (pilot): 3–5 finished 45–60s vertical videos as a test batch. Each one must follow its specific prompt and pass the QC checklist. If this goes well, there will be ongoing batches. Budget / pricing: Low cost is paramount – this is a concept validation phase. Please give your price per 45–60s video and your price for an initial batch of 3–5 videos. Ideal candidate: Short‑form video editor or UGC ads editor with examples of Reels/TikTok/Shorts, product/service ads, or testimonial‑style videos. Comfortable working from prompts, reference files and checklists. Fast, reliable, good communication, and able to keep costs low. Nice to have: experience with trades, home services, or performance‑focused social ads. Please include in your proposal: 3–5 examples of vertical short‑form videos you’ve edited. Tools you use (CapCut, Premiere, Final Cut, mobile, AI, etc.). Typical turnaround time per video. Your per‑video rate for ongoing batches (5–10 at a time).