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I have a curated dataset of abdominal X-ray images that needs a robust deep-learning model capable of classifying key clinical findings. The end goal is a production-ready Python solution that can consistently score above 90 % accuracy on an unseen validation set. You’ll start with any mainstream framework you prefer—TensorFlow, Keras, or PyTorch—and handle the full pipeline: data preparation and augmentation, model architecture selection, training, hyper-parameter tuning, and evaluation. Please keep the code modular and well-commented so I can retrain or fine-tune later as new data comes in. A concise report that explains your decisions, metrics, and suggestions for future improvements will also be appreciated. To help me choose quickly, focus your proposal on your exp...

I need a complete machine-learning pipeline that can look at medical images—specifically plain-film X-rays—and tell me whether each study is of the chest, abdomen, or an extremity. All input files will be standard hospital exports (mostly DICOM, occasionally PNG/JPEG), so the model must handle typical variations in resolution and contrast. What I’m after is a reproducible, well-documented solution: data preparation, augmentation, model architecture (a CNN in TensorFlow, Keras, or PyTorch is fine), training, and evaluation. Please include class-balanced splits, explain any preprocessing you apply, and show the metrics you achieve on an unseen validation set. Deliverables • Python code with clear comments for preprocessing, training, and inference • Trained ...

I have a collection of X-ray studies and I need a robust deep-learning model that can look at each image and instantly tell me which predefined category it belongs to (e.g., chest PA vs. chest lateral, cervical spine, hand, etc.). The job is strictly about classifying the type of X-ray, not diagnosing any pathology. Here is what I already have and what I expect from you: • A curated folder structure with several thousand labelled PNG and DICOM files that you can download from my secure server. • A preference for Python with either PyTorch or TensorFlow/Keras—use whichever framework you feel will achieve the best accuracy and fastest inference on a modern GPU. • Clean, reproducible code (Jupyter notebook or script) plus a short README that explains environment se...

I need a small repo / script that turns the whole OpenClaw + Telegram + LLM stack into a single-command experience. When a user supplies nothing more than their Telegram bot token (and an OpenRouter key for Deepseek), the script should: 1. Pull the latest stable release of OpenClaw. 2. Build and run it inside a Docker container. 3. Spin the container up on Railway via their CLI/API, setting all required environment variables automatically. 4. Wire the bot to speak English only and route every prompt through Deepseek served by openrouter. I am expecting: • A clean Dockerfile (or docker-compose) and a Railway template that work out of the box. • A (or similar) that asks for the two keys and does everything else unattended. • Brief inline docs/readme so any n...

​I am seeking a highly skilled AI technician to develop high-fidelity LoRA models for FLUX.1 [dev] and Wan 2.1. The goal is to achieve [insert goal: e.g., high character likeness / a specific artistic style] that remains consistent across both high-resolution images and video generation. ​Required Technical Expertise ​Model Experience: Proven experience training LoRAs for FLUX.1 (Dev/Schnell) and Wan 2.1 (specifically the 1.3B or 14B models). ​Tooling: Proficiency with Ostris AI-Toolkit, Musubi-tuner, or Kohya_ss. ​Dataset Management: Ability to curate and caption high-quality datasets (images for Flux, video clips for Wan). Experience with Danbooru/WD14 tagging or natural language captioning (LLM-based). ​Temporal Consistency: For Wan 2.1, you must be able to demonstrate that the LoR...

My goal is to push our Natural Language Processing stack forward by continually scouting, testing, and refining state-of-the-art models in three core areas: text generation, sentiment analysis, and machine translation. Scope of work — Track current research and emerging repositories (Hugging Face, arXiv, GitHub) to spot promising architectures and training techniques. - klaud8 / hrm ai / chat gpt / claude — Spin up controlled experiments in Python using PyTorch/TensorFlow, comparing baseline performance with fine-tuned variants on representative datasets. — Optimise inference speed, memory footprint, and prompt-engineering workflows so models transition smoothly from notebook to production API. — Document findings in concise experiment reports and integra...

I'm seeking an experienced AI developer to create a computer vision model focused on detecting people. The model will need to function effectively in both indoor and outdoor environments. Key Requirements: - Primary function: Object detection with a focus on people - Adaptable to both indoor and outdoor settings - High accuracy and reliability Ideal Skills and Experience: - Expertise in AI and machine learning - Strong background in computer vision, particularly in object detection - Experience with datasets and training models for varied environments - Proficiency in programming languages such as Python, and familiarity with libraries like TensorFlow or PyTorch Please provide examples of similar projects you've completed.

We have a platform that requires user to do a face capture, means user will take a live picture of themselves The goal is to ensure that only real users use our system. Let me know if you have done anything like this or capable

Key Responsibilities: • Design, develop, and deploy AI/ML solutions end-to-end • Lead AI architecture and solution design for enterprise applications • Build and optimize machine learning and deep learning models • Deploy and monitor models in production environments • Collaborate with cross-functional teams including product and engineering • Mentor junior AI engineers and contribute to technical leadership • Conduct research and implement state-of-the-art AI techniques • Ensure data quality, security, and model performance optimization Required Skills & Qualifications: • 10+ years of experience in AI/ML or Software Engineering roles • Strong proficiency in Python and data processing libraries (NumPy, Pandas) • Hands-on experienc...

I want to build a Windows-only application that watches the live screen, detects people—including stylised in-game characters—in real time, and immediately drives a game controller so the crosshair locks onto and tracks that target as it moves. The workflow I picture is straightforward: the program captures frames, an AI model spots the person, and a virtual stick signal (XInput, vJoy or a comparable driver) nudges the aim every frame so it stays centred. Smoothness and speed are critical. On a 1080p feed I’m aiming for roughly 60 fps with no more than 40–50 ms end-to-end latency, so techniques such as YOLOv8, TensorRT or a lightweight custom network combined with OpenCV screen capture should fit. You’re free to choose Python, C++, or another language as long...

I already have a working object-detection pipeline written in Python, and I now need that same logic moved into a cleaner, better-structured Python codebase that’s easy to maintain and integrate into a larger application. Think of it as a conversion/refactor: exact same model, exact same results, but with modern syntax, clear separation of concerns, and thorough inline comments. You’ll start from my original scripts and checkpoints, preserve every bit of accuracy, and hand back a fully functioning module (including a simple demo script) that can be installed with pip-installable requirements. Feel free to streamline library calls—TensorFlow, PyTorch, OpenCV, or whatever is currently in place—so long as the final inference output matches the reference I provide. De...

I need an expert to develop embedded algorithms for our OWLY predictive maintenance platform. The platform uses fused vibration and acoustic sensing to detect early equipment faults in home appliances, specifically washing machines, dryers, and refrigerators. Key Requirements: - Convert sensor data into actionable equipment health diagnostics. - Focus on early fault detection for motors, bearings, and gearboxes. Ideal Skills and Experience: - Strong background in embedded systems and algorithm development. - Expertise in signal processing and sensor data analysis. - Experience with home appliance maintenance or diagnostics. - Familiarity with machine learning techniques is a plus.

I have a small batch of images—fewer than one-hundred—that need clean, consistent object-detection labelling. For each image you will draw tight, non-overlapping bounding boxes around every instance of the target classes I will supply once we start. Accuracy matters more than speed; missed objects or sloppy boxes will be rejected. Preferred workflow is any modern tool that can export to COCO JSON or Pascal-VOC XML, as these formats plug straight into my training pipeline. If you normally use LabelImg, CVAT, Supervisely, or similar, that’s perfect. Deliverables • Annotated dataset in COCO JSON or Pascal-VOC XML (your choice, just stay consistent). • A quick text report summarising class counts and any edge cases flagged during labelling. I will run a...

I have a set of roughly 51 – 200 technical diagrams that must be fully prepared for computer-vision training. Each image needs three things: clear class labels, accurate bounding boxes around every object of interest, and pixel-level segmentation masks. You may work in any modern annotation platform you prefer—CVAT, Labelbox, VGG Image Annotator, or a comparable tool—as long as the final export is delivered in COCO JSON (or another widely used format we agree on before you begin). Consistency is critical; every diagram must follow the same labeling taxonomy and colour scheme so the dataset can be dropped straight into a model pipeline without extra cleaning. Deliverables • Labeled, boxed, and segmented versions of each diagram (51–200 images) • One...

I already have a working Python script that identifies stripe-like patterns in still images, but it needs to move from “proof-of-concept” to a polished, deployable module. The current model does a reasonable job on simple samples, yet its accuracy drops with noisy backgrounds, it only understands a handful of stripe geometries, and it processes large batches slower than I’d like. The brief is straightforward: • Improve accuracy: fine-tune the existing algorithm—or replace it—so it handles challenging lighting and mixed-texture scenes without a spike in false positives. • Add more pattern types: extend recognition beyond the basic horizontal/vertical stripes to oblique, curved, or irregular banding the current code ignores. • Optimize perfor...

I have safety sector time-series dataset that combines three synchronized streams: sensor imagery, textual maintenance logs, and high-frequency numeric readings. The objective is to forecast future values—not merely detect anomalies—so grid operators can anticipate demand, equipment stress, and renewable supply fluctuations. Because this is a research-level effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines. Key expectations • End-to-end experimentation code (Python, PyTorch or TensorFlow) with clea...

I have a kaggle dataset containing colored images and thermal image .. do feature extraction and then combine them and the do feature extraction on it

I have safety sector time-series dataset that combines three synchronized streams: sensor imagery, textual maintenance logs, and high-frequency numeric readings. The objective is to forecast future values—not merely detect anomalies—so grid operators can anticipate demand, equipment stress, and renewable supply fluctuations. Because this is a research-level effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines. Key expectations • End-to-end experimentation code (Python, PyTorch or TensorFlow) with clea...

Busco un profesor nativo de español que imparta clases avanzadas exclusivamente a adultos. El objetivo central es potenciar la conversación fluida, reforzar la gramática de nivel C1-C2 y preparar a los estudiantes para exámenes oficiales. Qué necesito de ti: • Sesiones online de 60 min, 2-3 veces por semana, vía Zoom o Google Meet. • Materiales personalizados (presentaciones, ejercicios y lecturas) alineados a cada objetivo. • Retroalimentación escrita y breve informe de progreso cada cuatro clases. • Flexibilidad horaria — preferencia por tardes/noches GMT-5. Requisitos clave: – Experiencia demostrable enseñando español avanzado a adultos. – Dominio de técnicas de conv...

The project centres on building a production-ready text-classification pipeline that leverages modern deep-learning techniques. I have a labelled dataset and need end-to-end code that ingests the text, handles cleaning and tokenisation, and trains an accurate classifier. Python is the preferred language; using PyTorch, TensorFlow or another mainstream framework is fine as long as the solution is reproducible and easy to extend. Key deliverables: • Well-commented source code (data loading, model, training loop, evaluation) • Clear instructions to run training on a fresh machine (README or notebook) • Metrics report showing accuracy, precision, recall and F1 on a held-out set • Exported model weights and a small inference script or API endpoint for batch prediction...

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A comprehensive literature review on recent developments in artificial intelligence is required for a science-focused research paper. The goal is to synthesise peer-reviewed work published roughly between 2020 – 2024, highlight prevailing trends, identify research gaps, and propose future directions. No experimental data collection or purely theoretical exposition is needed—this piece is strictly a literature review. I will supply a preliminary outline and a small set of key papers. You will expand the search using scholarly databases (Google Scholar, IEEE Xplore, ACM Digital Library, Scopus) and apply clear inclusion/exclusion criteria. Please organise the discussion around major AI sub-fields—such as deep learning architectures, reinforcement learning, ethical AI, and ...

Responsible for initiating the implementation and application of Data Science strategies to enhance company results. Some of the projects developed: - Time Card Information Extraction Automation via OCR Developed an OCR pipeline to automatically extract and process information from time cards and integrate it into the company's system; Reduced processing time and minimized manual errors in employee time management; - Intelligent Chatbot for Internal Support Modeled an intelligent chatbot trained on the company manual, utilizing LLMs with optimized prompts, Transformer-based architecture via Hugging Face, RAG (Retrieval-Augmented Generation) approach, Redis for context caching, and Gunicorn as the application server for production; - Inventory Prediction and Purchasing Optimization...

I’m building a production-grade image-enhancement pipeline and want an AI engineer who lives and breathes Python and computer vision. The goal is to take raw mobile photos and automatically clean noise, sharpen details, fix color and convert them to 3d printable files. You’ll choose or design the model, train or fine-tune it, then wrap everything in a lightweight API that my mobile team can call in real time (on-device when feasible, cloud fallback when not). You should be completely comfortable with OpenCV plus deep-learning frameworks such as PyTorch or TensorFlow, and you know the trade-offs between traditional filters, GAN-based approaches, and modern super-resolution networks. Experience packaging models for CoreML, TensorFlow Lite or similar mobile runtimes will set you ...

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...

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...