Deep Learning Jobs

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...

Machine Learning Framework & Automated Dataset Generation for FANET Jamming Detection (NS-3) Hello, I am currently working on a research project titled: “Intelligent Jamming Detection in Flying Ad Hoc Networks (FANETs) Using NS-3 Simulation and Machine Learning.” I have already completed the NS-3 simulation phase and now need support with the machine learning, automated dataset generation, and data processing phase. ⸻ What Has Already Been Completed * Built FANET scenarios in NS-3 * Implemented the Gauss-Markov mobility model * Implemented multiple jamming attack types: * Reactive jammer * Hybrid jammer * Constant jammer * Random jammer * Executed simulations under multiple network conditions * Generated network performance metrics from NS-3 outputs ...

Responsibilities: Guide me on how to use Snorkel AI or Snorkel Flow for my training task Help design and implement labeling functions Assist with data labeling strategy and weak supervision setup Explain how to prepare data for model training Help train, evaluate, and improve the model workflow Provide hands-on support through screen sharing or step-by-step guidance Review my current dataset and recommend the best Snorkel approach Required Skills: Strong experience with Snorkel AI, Snorkel Flow, or open-source Snorkel Experience with weak supervision and programmatic labeling Python experience Machine learning model training experience Experience with classification, NLP, text labeling, or data annotation workflows Ability to explain clearly and guide someone step by step Deliverables:...

I have a complete OCT (optical coherence tomography) image set and now need to turn it into a full, publication-ready study on age-related macular degeneration (AMD). The work has to run entirely in Google Colab and revolve around a hybrid deep-learning architecture of your choice—CNN + transformer, ensemble CNNs, or any comparable combination—as long as it meets strong SCI journal standards. Pre-processing Both FFT and Wavelet Transform must be applied. Please document each step in the notebook so the signal-processing pipeline is clear and reproducible. Core modelling • Train, validate and test the model on the OCT data. • Track and store all metrics so they can be plotted later. • Incorporate Explainable AI focused on feature-importance visualisation...

I have a single 40-second MP4 clip that shows two motorcycles circulating the same track. Each bike can be separated at a glance because they are painted different colours. What I need is a reliable, frame-accurate measurement of the time interval between the first and the second motorcycle as they pass a chosen reference line on the circuit. Please use YOLO (or an equivalent real-time object detector) to: • detect both bikes throughout the whole sequence, • define a consistent reference line or region on the track, • timestamp the exact moment each bike crosses that reference, and • burn a clear visual overlay onto the video that displays the calculated gap in seconds. The finished deliverable is the processed video with the overlay already embedded; no separa...

I am in the middle of my master’s thesis and need a capable partner to handle the coding side of a deep-learning project on mammogram images. The core task is to build a robust model for breast-cancer diagnosis; once the network architecture is in place I can take care of statistical write-ups and broader discussion, but I need you to get the codebase production-ready and well-documented. Here’s what the collaboration will look like: • Data: I will supply the curated mammography dataset and any preprocessing scripts I have so far. • Model building: Using Python with either TensorFlow or PyTorch (whichever you work fastest with), you will design, train and fine-tune a CNN or transformer-based pipeline that targets breast-cancer detection specifically. • Resu...

I need to turn a Large Language Model into a practical, real-time assistant that runs directly on an NVIDIA Jetson board. The model will reason over image data at the edge, so every millisecond saved and every megabyte spared matters. Memory-optimised execution is therefore the single most important constraint, though I still expect you to keep latency low and power draw sensible. Here is what I want to achieve. The LLM must accept a pre-processed visual input, apply prompt-engineering tricks that preserve context, and reply fast enough to be useful on-device—no cloud fallback. Smart caching, selective quantisation, and, when it genuinely pays off, lightweight fine-tuning are all on the table. I will look to you to suggest the right mix of techniques and to implement them. You shou...

Machine Learning Framework & Automated Dataset Generation for FANET Jamming Detection (NS-3) Hello, I am currently working on a research project titled: “Intelligent Jamming Detection in Flying Ad Hoc Networks (FANETs) Using NS-3 Simulation and Machine Learning.” I have already completed the NS-3 simulation phase and now need support with the machine learning, automated dataset generation, and data processing phase. ⸻ What Has Already Been Completed * Built FANET scenarios in NS-3 * Implemented the Gauss-Markov mobility model * Implemented multiple jamming attack types: * Reactive jammer * Hybrid jammer * Constant jammer * Random jammer * Executed simulations under multiple network conditions * Generated network performance metrics from NS-3 outputs ...

I have a complete OCT (optical coherence tomography) image set and now need to turn it into a full, publication-ready study on age-related macular degeneration (AMD). The work has to run entirely in Google Colab and revolve around a hybrid deep-learning architecture of your choice—CNN + transformer, ensemble CNNs, or any comparable combination—as long as it meets strong SCI journal standards. Pre-processing Both FFT and Wavelet Transform must be applied. Please document each step in the notebook so the signal-processing pipeline is clear and reproducible. Core modelling • Train, validate and test the model on the OCT data. • Track and store all metrics so they can be plotted later. • Incorporate Explainable AI focused on feature-importance visualisation...

I'm looking for a ComfyUI expert to build 2 custom workflows for my AI video production pipeline on RunPod. Workflow 1 — LoRA Training - Set up a working LoRA training workflow (Kohya or FluxGym) - Based on Flux model - I will provide 20-50 training images of my character - Output: trained LoRA file ready to use in ComfyUI Workflow 2 — WAN 2.2 Video Generation - WAN 2.2 I2V or T2V workflow in ComfyUI - Format: 9:16 (480x832), 8 seconds - LoRA slot integrated so my character stays consistent - Upscaler included - Must be simple enough for a VA to operate: just change the prompt and hit Run Please only apply if you have real hands-on experience with ComfyUI, LoRA training, and WAN 2.2.

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...