Deep Learning Jobs

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Hybrid Artificial Intelligence System for Autonomous Drone Detection Using Computer Vision and RF Signal Analysis The idea is to build AI detection system based on hybrid method (Computer vision and RF signal) and present results :Accuracy, Precision, Recall and False Alarm Rate Algorithms: Computer vision: YOLOv8 (Object Detection) RF analysis: CNN for Spectrogram analysis Merge in : Rule-Based Fusion • or Neural Network Fusion Dataset : Vision Datasets: • Anti-UAV Dataset • VisDrone RF Datasets: • DeepSig RadioML Dataset • Custom SDR Capture (HackRF / RTL-SDR) Budget : 150$

We are looking for a talented frontend developer to bring our Figma designs to life by building a visually stunning, high-performance website using HTML, CSS, and JavaScript. This project involves translating Japanese content, so proficiency in Japanese is required. The ideal candidate will have a strong eye for design and be able to accurately replicate layouts while enhancing them with smooth animations and professional styling. Responsibilities: Convert Figma designs into clean, responsive HTML/CSS/JavaScript code Ensure pixel-perfect implementation aligned with the original design Translate Japanese content accurately into the website Implement modern UI animations and interactions Optimize for performance and cross-browser compatibility Requirements: Proven experience in frontend deve...

I have just 700 indoor photos that must be labelled for an my project upcoming object-detection model. Every visible person and every seat that is currently occupied needs its own bounding box, and because the network will train on oriented-bounding-box (OBB) data, the rectangles have to follow the exact rotation of the body or seat—especially when someone is leaning. Data is of infrared camera of a cinema check samples , please note there can be many people in one image i want each to be marked properly You will work inside the simple browser-based annotation tool I built for this purpose or any other annotation tool you like. drag the box corners, spin the angle handle where needed, hit save, and move to the next frame; the app tracks progress automatically so nothing is missed....

I have a large, continually growing collection of emails that needs to be processed automatically. The goal is twofold: 1. Classify each email into predefined business categories with high accuracy. 2. Extract relevant entities (names, dates, IDs, product references, etc.) from the same messages. You will own the entire machine-learning workflow. That means cleaning and exploring the raw email text, crafting useful features, training and tuning your models, and packaging the final solution behind an API that I can call from our existing back-end. Python is a must, and I’m comfortable with either TensorFlow or PyTorch for the deep-learning components—use whichever lets you move fastest. Traditional techniques with Scikit-learn are welcome wherever they make sense. Because t...

I need an experienced Natural Language Processing specialist to build a predictive model that learns from text data and produces accurate, repeatable forecasts. The project centers on end-to-end model creation: cleaning and preprocessing raw text, selecting the right architecture, training, and evaluating performance against clearly defined metrics. I will supply the text dataset along with the target variable I want predicted. You will decide on the most suitable NLP approach—whether that’s classical techniques with scikit-learn or a deep-learning stack such as TensorFlow or PyTorch—document your reasoning, and implement the solution in clean, well-commented Python. Deliverables: • Reproducible code (Jupyter notebook or .py files) with setup instructions • ...

deploy a dual-model pipeline on AWS. Scope of Work Dual-Model Deployment: Deploy the Pillar-0 (Atlas architecture) for multi-finding classification across Chest, Abdomen, and Brain. Integrate Sybil-1.5 for specialized future lung cancer risk prediction (1–6 year horizon). Inference Pipeline & Report Generation: deploy pipeline that takes zipped DICOM files, performs 3D volumetric reconstruction, and runs concurrent inference. script (attached). This script shows how to read the (Zip file), stack of the medical pictures (DICOMs) into a 3D block, and run both the Pillar-0 model (for findings) and Sybil-1.5 (for cancer risk) -This tells AWS which specialized tools to install to read 3D medical images AWS Cloud Architecture: Host models on AWS SageMaker (GPU instances like m...

Contest: Real-Time Lipsync Avatar from a Single Photo — POC / Skills Test Important: This is a Paid Proof of Concept This contest is a skills assessment. We are looking for a talented engineer to join a multi-week (potentially multi-month) project to build a full real-time avatar platform — similar in quality and capability to HeyGen, LiveAvatar, Replika, Candy AI, and D-ID. The winner of this contest will be offered a long-term contract to build the full pipeline with us. Do NOT apply if you can only deliver pre-rendered video. We need real-time. What We Need (POC Deliverable) Build a working prototype that does the following in real time: Take a single static photo (portrait/face) as input Take a live audio stream (microphone or audio chunks) as input Output a video stream ...

I’m building ViewSeek AI as a full-featured web application that puts advanced artificial intelligence directly in the browser. The core of the product is AI itself, so every major workflow must be driven by machine-learning models and thoughtful UX. Here is what the first release needs to deliver: • Image recognition that can tag, classify and return metadata in near real-time. • Natural language processing so users can type questions, receive context-aware answers and carry on multi-turn chats (“Asking AI”). • Predictive analytics modules that surface trends or recommendations based on uploaded data sets. • Generative tools capable of creating both images and short video clips from text prompts, with download options in common formats. All ...

The project centres on building a production-ready medical image -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; The preprocessing must involve Quantum computing techniques using Pennylane. 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 • Export...

AI & MACHINE LEARNING TRAINING MANUAL DEVELOPMENT We are urgently seeking a highly qualified Expert Trainer to develop a comprehensive, hands-on Manual and deliver specialized training to Customs of an African country. This project aims to enable the beneficiary organization to autonomously design, optimize, and deploy AI/ML solutions for risk management and customs clearance efficiency. Scope of Work: Develop curriculum and content focusing on advanced technical modules, including: Data Science & Machine Learning: Deep Learning and Python Data Science foundations. Generative AI & LLMs: Advanced Prompt Engineering, Fine-tuning, NLP/Transformers, and API Deployment (LangChain). Methodology: Emphasize practical application, real-world customs use cases, and Capstone projects (...

I am working on an academic research project focused on multi-cloud resource orchestration using a decentralized framework called IMACO (Intelligent Multi-Agent Cloud Orchestration). Currently, I have implemented a Python-based simulation system that includes: Task scheduling using real workload data (Google Cluster Workload Traces) Existing scheduling strategies: Round Robin (RR) Cost-based scheduling IMACO (adaptive rule-based scheduler) objective : I want to extend the system by implementing: 1. MAS-Cloud+ (Multi-Agent System) Each cloud provider should act as an independent agent Agents evaluate tasks using scoring logic (cost, latency, load, SLA) A coordinator selects the best agent for task allocation Output must match existing format for comparison 2. Reinforcement Learning Inte...

I am preparing a scientific-grade computer-science paper that explores how Artificial Intelligence can be applied to Disease Diagnosis, with a sharp focus on Genetic Disorders. I already have a broad outline but need a researcher-writer who can turn it into a publishable manuscript that meets typical IEEE or Springer journal standards. Here’s what I’m after: • Scope. A clear literature review on AI techniques currently used for diagnosing genetic conditions such as cystic fibrosis, sickle-cell, or rare chromosomal abnormalities. Contrast traditional pipelines with cutting-edge deep-learning approaches (CNNs, Transformers, multimodal models, etc.). • Original contribution. Either propose a novel framework or run a small-scale experimental study on an open dataset (...

I run several cooking blogs and want to automate the creation of clean, consistent ingredient photos. Your task is to train (or fine-tune) an image-generation model so that it reliably produces minimalistic ingredient shots laid out on a wooden surface. Here’s what I need from you: • Help me assemble, clean, and label a high-quality training set of ingredient images that match the desired look. • Select or build the best model architecture (Stable Diffusion, DreamBooth, or a comparable approach)—I’m open, as long as it learns quickly and can run locally after training. • Train and validate the model, then deliver the final weights, a straightforward inference script, and a short guide so I can generate new photos on demand (Python preferred). &bu...

• Description Video frames will be fed into the engine, and the backend API will return certain information: 1. Types of sports What sport is it? Football/basketball/rugby/American football, etc 2. List of Logos Detected logos: UEFA, Real Madrid, Bayern Munich, Liverpool, Inter, etc 3. List of players Identify players from the shirt: 10 Kylian Mbappe, 11 Rodrygo, 12, 13 … 4. Home/away From the players’ wears: Real Madrid home/away, Liverpool home/away 5. Match info (team and score) on the top left corner of the screen (if there is any) Upon a worker's initiation and processing, your engine will store the identified information in memory and send it back to the front end. The worker can be re-initiated/reused. For training purposes, there will be the following ...

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I need a complete Automatic Number Plate Recognition solution that accurately reads both car and motorcycle plates during daylight hours. Once a plate is captured, the system must do two things automatically: log the full plate data (image, text, timestamp) into a database and trigger an immediate alert through a simple webhook or REST call. My ideal stack is Python with OpenCV or a comparable vision framework, paired with a modern detection model such as YOLOv8-seg, but I am open to alternatives if you can demonstrate equal or better accuracy. You are free to use an off-the-shelf OCR engine (Tesseract, EasyOCR, PaddleOCR) or a custom-trained model, as long as you meet the accuracy target. Key deliverables • Optimized plate-detection and OCR pipeline tuned for daylight footage &bu...

Our client runs a call center with Nigerian agents calling US businesses. They need a Python script that makes the agents' accent sound more neutral and clear to American ears in real time during live phone calls. The hard part is already done. There is a fully built Node.js server that handles the live call audio. We just need you to write one Python script that plugs into it. What the script needs to do: Receive live audio from the server, process it through a voice conversion model to soften the Nigerian accent, and send the processed audio back. The whole process must happen in under 150ms so the conversation feels natural with no delay. Tech details: Audio comes in as mulaw 8kHz chunks. You can resample internally for better model performance then convert back before sending ...

Looking for a highly skilled Data Scientist with deep expertise in AI and Generative AI to lead cutting-edge retail merchandising and pricing analytics initiatives, driving scalable forecasting, optimization, and intelligent automation solutions. -Leverage state-of-the-art AI and Generative AI techniques to drive innovation in retail merchandising and pricing analytics. -Design, develop, and deploy advanced machine learning and optimization models to support data-driven business decisions. -Build scalable solutions for demand forecasting, assortment optimization, price elasticity modeling, and inventory allocation and replenishment. -Develop sophisticated demand forecasting systems using multivariate and hierarchical models for long-range, multi-echelon predictions. -Address complex f...

I’m assembling a small, highly-skilled development squad to finish a platform that sees the world, talks to external services that never published their docs, and then deploys itself at scale. Python sits at the heart of the codebase, so deep confidence with modern Python, virtual environments and dependency management is essential. Most of the heavy lifting in computer vision happens through OpenCV and complementary deep-learning libraries; the pipeline must handle object detection, image processing and facial recognition with equal reliability and speed. If you also think in C++ or Java when performance demands it, that flexibility will be welcome. Beyond vision, we have to collect data from third-party applications that do not provide friendly endpoints. That means reverse-e...

I have a proprietary image dataset and I need a seasoned computer-vision engineer to turn it into an end-to-end solution that can 1) recognise each image, 2) detect all relevant objects inside it, and 3) optionally segment those objects when that adds value to the overall result. The primary goal is accurate detection and classification, but I’d like the system to be flexible enough to switch on pixel-level segmentation whenever it boosts performance. Here is what I’m after: • A clean Python pipeline (PyTorch or TensorFlow preferred, with OpenCV for preprocessing) that ingests my custom images, trains suitable models, and exposes an easy inference script. • Clear evaluation: confusion matrices, mAP/IoU scores, and plots of loss/accuracy over epochs so I can judge...

Project Title AI-Powered Textile Design Processing Tool (Image-to-Layered PSD System) ⸻ Project Overview We are building an AI-powered system for the textile and digital printing industry that can convert high-resolution flattened images (e.g., garments on models) into fully editable layered files (PSD format). The goal is to enable textile manufacturers and designers to: • Extract individual design components (patterns, colors, textures) • Edit them independently in tools like Photoshop • Use outputs directly for production and printing workflows This project is focused on accuracy, scalability, and production-readiness, not just a demo-level solution. ⸻ Objective Develop a system that can: 1. Take a high-resolution input image (300 DPI, large dimensions) 2. A...

The AI Medical Diagnosis Chatbot is an intelligent healthcare assistance system developed using Machine Learning, Natural Language Processing (NLP), and Conversational AI technologies. The main objective of this project is to help users analyze symptoms, predict possible diseases, and generate preliminary medical guidance through a conversational chat interface. The chatbot interacts with users in a human-like manner by accepting symptom-related queries in natural language, such as fever, cough, headache, fatigue, or other health concerns. Using trained machine learning models, the system processes the user’s symptoms and predicts the most probable disease or medical condition. The project integrates NLP techniques to understand user input, extract symptoms, and convert them into a...

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...