Học Sâu Jobs

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

Vui lòng Đăng Ký hoặc Đăng Nhập để xem thông tin.

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...

Lead AI / Fullstack Engineer — Project "AZIZA" (Voice-to-Voice AI) ​Project Name: AZIZA Format: Project-based / Remote (with access to local GPU clusters) Tech Stack: PersonaPlex (Moshi-based architecture), PyTorch, TensorRT-LLM, FastAPI, WebRTC, Telegram Mini App (TMA). Hardware Location: Uzbekistan & Turkey clusters powered by NVIDIA L40S ​Project Overview ​AZIZA is an innovative multimodal "Speech-to-Speech" (S2S) ecosystem designed to simulate natural human interaction. We are building an AI assistant that seamlessly transitions between roles: an expert tutor (Chemistry, History, Biology), an empathetic companion, and a simultaneous translator. By processing audio tokens directly, the system achieves unprecedented interaction speeds. ​Current Status: The ...

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...

Vui lòng Đăng Ký hoặc Đăng Nhập để xem thông tin.

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 ...

I'm looking for an experienced Python teacher specializing in advanced programming and machine learning. Essential requirements: - Expertise in Python, especially in machine learning - Ability to explain complex concepts clearly - Experience with popular ML frameworks (e.g., TensorFlow, PyTorch) - Flexible scheduling to accommodate my learning pace If you have a strong background in these areas, please apply!

At I am expanding an internal project that explores fresh ideas in machine learning and need an intern who can jump straight into the research cycle—formulating hypotheses, building quick prototypes, running experiments, and summarising insights for the team. What you will do • Survey recent ML literature that aligns with our roadmap • Design and execute experiments to test new model architectures or training tricks • Record results, visualise them clearly, and explain the “why” behind the numbers • Iterate on your findings until we have a result worth integrating into production-grade code What you should bring Your application only needs to highlight relevant experience, but that experience must show strong analytical and problem-solving...

I’m running a research-grade study on how different transfer-learning strategies affect deep-learning image-classification accuracy and efficiency, and I’d like a PyTorch expert to help me turn the experimental plan into clean, reproducible code and an insightful report. Here’s what I need from you: • A well-structured PyTorch pipeline that can load a few supplied image datasets, swap in multiple pretrained backbones (e.g., ResNet, EfficientNet, ViT) and fine-tune them under several common strategies (feature extractor, full fine-tune, layer-freezing variants). • Training scripts or notebooks with clear logging of hyper-parameters, metrics, and run times so results are comparable across experiments. • A concise written analysis summarising finding...

I have a growing catalogue of raw video assets that need to be delivered in several sizes, bit-rates, and formats, and I want the entire pipeline handled through FFmpeg. In parallel, I’m exploring AI-driven animation—think taking existing footage or image sequences and adding character motion, stylised effects, or scene enhancements powered by a modern deep-learning model. Here’s what I need from you: • Design and script an FFmpeg workflow that ingests source files and outputs clean, artifact-free H.264/H.265 versions at multiple resolutions. It should be efficient enough for batch processing on a single workstation but scale to a GPU server when required. • Recommend (and set up) an AI model suited to my animation goals, then wire it into the pipeline so...

Looking a simple python script for voice pipeline that is fully local (offline), runs on Windows (Core i7), processes microphone audio in real time, uses VAD to detect end-of-speech, performs streaming STT, and immediately streams the recognized text into TTS for echo playback. End-to-end latency must be under 1 second. Any open-source stack that meets this latency is welcome

We’re building Inboxx, a business messaging platform (SMS / chat / voice). The backend + chat service is already built. We need an AI engineer to plug in the brain — not run the company. This is NOT a full-stack or infra role.

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...