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I have a collection of short-film scenes where I need to replace a single human performer with an AI-generated double. You will receive the original footage (4K ProRes) together with reference images of the actor who should appear on screen. Your task is to create a seamless, frame-accurate replacement that preserves lighting, eyelines, facial expressions, and dialogue sync so the edit still feels natural. You are free to use DeepFaceLab, FaceFusion, Stable Diffusion, Runway Gen-1 or any other proven toolset, as long as the result holds up on a cinema screen. Colour matching and grain must remain consistent with the plate, and there can be no facial warping or flicker under motion. Deliverables: • Final graded shots in the original resolution and frame rate • A side-by-side c...

I am planning a sustained series of Artificial Intelligence studies that will be submitted to peer-reviewed journals and conferences. For each project I will provide the core research question, relevant datasets (or simulation parameters), and any preliminary experimental results. Your task is to transform that material into a full, publication-ready research paper that meets the formatting and methodological standards of venues such as IEEE, ACM, or Springer. Typical flow • You review the brief, confirm the research hypothesis, and propose the structure of the paper. • Once we agree on the outline, you expand it into an 8-12 page manuscript in LaTeX (preferred) or Word, complete with abstract, introduction, related-work section, methodology, experiments, discussion, conclus...

I have a collection of short-film scenes where I need to replace a single human performer with an AI-generated double. You will receive the original footage (4K ProRes) together with reference images of the actor who should appear on screen. Your task is to create a seamless, frame-accurate replacement that preserves lighting, eyelines, facial expressions, and dialogue sync so the edit still feels natural. You are free to use DeepFaceLab, FaceFusion, Stable Diffusion, Runway Gen-1 or any other proven toolset, as long as the result holds up on a cinema screen. Colour matching and grain must remain consistent with the plate, and there can be no facial warping or flicker under motion. Deliverables: • Final graded shots in the original resolution and frame rate • A side-by-side c...

I'm Hiring | Academic Mentor – NLP Arabic Text Summarization (Part-Time / Per Task) Are you passionate about NLP, transformers, and Arabic language processing? We're looking for an expert academic mentor to guide the development of a Hybrid Arabic Text Summarizer using BERT and Transformer-based architectures. This is a flexible, per-task engagement — ideal for a researcher, graduate student, or academic professional looking to contribute to a meaningful NLP project on their own schedule. About the Project: I'm building a hybrid Arabic text summarization system that combines extractive and abstractive approaches, leveraging pre-trained BERT models (e.g., AraBERT, mBERT) and Transformer-based architectures. Your Responsibilities: ✦ Provide expert guidance on de...

For my in Robotics and AI, I need a full-fledged thesis that explores how artificial intelligence can enhance smart-city services and urban governance. The work must weave together a solid academic narrative with hands-on experimentation in Python, relying on TensorFlow as the main library for model development and analysis. Your writing should move from an up-to-date literature review into well-structured case studies and end with reproducible simulation results. Along the way, every algorithm needs to be spelled out twice—first as clear pseudocode, then as thoroughly commented Python code. I will use this codebase for live demonstrations, so it must run out of the box and produce the same figures and metrics you report in the thesis. Deliverables • Complete thesis documen...

Over the next year I’ll be running a series of computer-vision initiatives that require solid, production-ready Python code. The work spans the full pipeline: cleaning and augmenting large-scale image datasets, architecting and training deep networks, then stress-testing their performance before deployment. Knowledge in quantum computing is an added advantage You’ll dive into convolutional and recurrent architectures for vision tasks—think hybrid CNN-RNN pipelines for sequence labelling—as well as exploratory projects where a GAN may generate synthetic images for data balancing and style transfer. Everything is image-based, so familiarity with libraries such as OpenCV for preprocessing and TensorFlow or PyTorch for model building is essential. What I need from you...

I’m ready to bring an online trainer on board who can guide an intermediate audience through real-world applications of Machine Learning, Deep Learning, Natural Language Processing, and some domain-specific AI tool know-how. Sessions will be delivered live, so I need someone who is comfortable explaining concepts clearly, fielding questions on the spot, and showing hands-on demonstrations in popular frameworks (think Python, TensorFlow, PyTorch, scikit-learn, Hugging Face … whatever fits the topic). Because we’d like to launch the program as soon as possible, you should already have slide decks, notebooks, and practical examples you can adapt quickly. A short diagnostic chat with me will finalise the exact schedule, but plan for multiple interactive sessions, each follo...

I want to push the Gemma 4 E2B model to its limits for my HA MCP tools by applying LoRa fine-tuning on a purpose-built dataset. All training data must come from manual data entries—no scraping or sensor logs—because I need full control over quality and privacy. Scope of the dataset • 500 single-tool examples for every HA MCP tool • 100 multi-tool workflow examples for each tool group • Overall target: roughly 15 k – 20 k well-structured prompts and ideal completions What I expect from you 1. Design an efficient schema that separates single-tool tasks from multi-tool workflows yet still feeds cleanly into the Gemma 4 LoRa pipeline. 2. Build and validate the dataset (CSV/JSONL preferred). 3. Implement LoRa fine-tuning on the Gemma 4 E2B base, it...

I need an AI engineer who can architect, train, and iterate on deep-learning models that perform both medical-imaging analysis and diagnostic support. The scope covers X-ray, MRI, and CT data, so you should be comfortable handling multimodal image pipelines and the differing pre-processing each modality demands. You will start from a clean slate: selecting or designing network architectures in Python, building them with PyTorch or TensorFlow, and setting up a repeatable training environment that lets us experiment rapidly. Once a strong baseline is in place, I want to see steady, research-driven improvements—new loss functions, data-augmentation ideas, self-supervised techniques, or anything that reliably drives accuracy upward while keeping the models clinically robust. Deployment...

I want to build an AI-powered solution that automatically enhances and retouches images. The core idea is to feed photographs into the system and receive back professionally polished results—improved sharpness, balanced exposure, refined skin texture, and overall visual pop—without manual intervention each time. Here’s what I need from you: • Design or adapt a suitable model (GAN, diffusion, or other state-of-the-art architecture you trust) focused on image enhancement and retouching. • Implement the full pipeline—pre-processing, inference, and post-processing—so I can drop in a batch of images and retrieve the retouched versions in a predictable folder structure. • Keep quality high: no lost details, no over-smoothing, colors must remain...

I need a reliable, camera-based solution that can automatically detect and count people captured by our existing CCTV network, regardless of whether the footage comes from indoor halls or outdoor entrances. The end goal is clear: turn those raw counts into actionable customer-analytics data I can slice by time, location, and traffic trend. Here’s what I’m expecting: • Accurate real-time or near-real-time counts from standard CCTV streams (no extra infrared hardware). • Robust performance under varying light and weather because some of our cameras sit outdoors. • An easy way for me to view and export daily, weekly, and monthly traffic reports—CSV download and a lightweight dashboard are perfect. • A straightforward deployment path: containerised code (...

I need a reliable pipeline that takes any pre-recorded video, swaps the on-screen face with a chosen target face, and keeps the lips perfectly synced to the original soundtrack. High accuracy is non-negotiable—phoneme-level alignment, natural mouth shapes, and seamless facial blending should hold up under close inspection and slow-motion playback. Preferred stack You’re free to combine proven solutions such as Wav2Lip, DeepFaceLab, FaceSwap, or custom GAN models, as long as the end result meets the visual standard. Python with PyTorch/TensorFlow is ideal because I want the option to retrain or fine-tune later, but an all-in-one executable is acceptable if thoroughly documented. Required deliverables • Use e.g. Kling AI, Wan 2.3 Runway ML Acceptance criteria •...

I’m looking for an experienced researcher–author to co-develop, write, and successfully submit a skin-disease–detection paper to a genuine, APC-free SCI-indexed journal and takes acceptance guarantee. Scope • Craft a manuscript that introduces and validates a brand-new detection algorithm. • Build experiments around whichever deep-learning model is currently most promising—CNN, transformer, hybrid, or any other trending architecture—so long as the results outperform or clearly benchmark against existing methods. • Handle all stages from dataset curation, model training, result analysis, and figure/table preparation through to journal formatting, cover letter, and submission. • Guide the paper through peer review until acceptance. ...

• Built StockAI, a full-stack AI-powered stock market analysis and trading platform using Python, FastAPI, Streamlit, XGBoost, and Machine Learning. • Developed dual-machine learning models for stock price forecasting and BUY/SELL signal generation using 5 years of historical market data. • Engineered 35+ advanced technical indicators including RSI, MACD, Bollinger Bands, ATR, Stochastic Oscillator, Williams %R, OBV, moving averages, volatility metrics, and momentum-based features. • Implemented feature engineering pipelines to transform raw OHLCV market data into predictive trading intelligence. • Built time-series forecasting systems with proper train-test separation, walk-forward validation, and backtesting to eliminate data leakage. • Created confiden...

Goal: Create a FULLY AUTOMATED process that takes a male audio file and converts it into a female voice. What you must do: 1) Take the male audio I provide 2) Convert it into a female voice 3) Upload the final audio into a Google Drive folder 4) Add the Google Drive link in your competition entry 5) Explain clearly what software/tools you will use 6) Explain clearly how you will automate the FULL process from start to finish Important: - The automation must run locally - The final voice must sound perfectly natural and human - The female voice must correctly reproduce the multiple emotions, tone and intonations from the original audio - The result must NOT sound robotic or AI-generated - The automation must be able to process multiple audio files - Do NOT clean the original audio more...

The project centers on building a production-ready TensorFlow 2.x model that classifies tabular data delivered to us through an internal API. I have the API specifications and sample payloads ready; you will turn those streams into a clean training pipeline, engineer the right features, and iterate until the classifier meets our performance targets in real-world tests. Scope of work • Data pipeline – pull the API data, handle preprocessing, and produce TensorFlow-friendly datasets for train/val/test splits. • Model development – design, train, and tune a deep learning architecture suitable for tabular inputs (e.g., wide & deep, Transformer, or other proven structures). • Optimization – experiment with hyperparameters, regularization, and callback...