Deep Learning Jobs

RGB-Based Computer Vision and Spectral Characterization of Semiconductor Samples ## Project Overview I am developing a Python pipeline to investigate whether standard RGB photographs can predict useful spectral properties of semiconductor samples. My dataset contains approximately 62 RGB images of sample plates, with repeated photographs of some physical plates, and associated diffuse reflectance spectra. Each plate contains 48 samples. Correctly mapping images, sample IDs, and reference spectra is essential. ## Scope of Work **1. Image Processing and ROI Extraction** - Detect and crop plates, preserving existing working code wherever possible. - Reliably extract 48 sample regions and handle both 6 × 8 and 8 × 6 layouts. - Improve orientation detection and consistent sampl...

Preferences: Share proof-of-concept/Prior domain work integrations are crucial while applying user proposals.

I# AI-Powered Lab Assistant and Companionship Platform Development ## Project Overview I am looking to develop a unique, end-to-end AI-powered platform with a primary focus on companionship and innovative user interactions. The goal is to create something distinctive that has not been widely explored in the current market, with the potential to expand its capabilities through additional features in the future. The primary objective is to develop a reliable and innovative AI model that supports meaningful companionship experiences. As part of the platform's longer-term vision, there may also be an option to introduce an adult-oriented component as an optional add-on for users who are of legal age and wish to explore their personal interests in a safe, responsible, and legally ...

I need an Al-focused application that delivers 100% functionality in analysing and solving geometric patterns and sub patterns and sub to sub pattern at any level in jewellery. The primary objective is to ensure precision and reliable output. The features include: 1. Detecting patterns across different jewellery parts such as any parts of boundaries, textures, cuts, or design elements (including broad patterns, sub-patterns, and intricate sub-to-sub patterns). 2. Identifying low-quality or unclear components within these patterns and seamlessly replacing them with high-quality elements from other sources or jewels. 3. Ensuring replacements naturally blend into the overall piece, maintaining structural integrity and visual coherence. The focus is entirely on achieving accurate fun...

I have an existing GPU-accelerated network component that still compiles, but its inference logic is tangled and hard to maintain. I want the codebase cleaned up and modernised while keeping the same outputs and speed. Here is what I need from you: • Compile the current project on a recent CUDA-enabled Linux environment so we start from a reproducible build. • Re-architect the inference path, shifting it to clear, modular functions that follow TensorFlow best practices. The focus is code refactoring, not new features. • Preserve existing model weights and performance; any change that alters numerical results must be explained. • Deliver a git repo containing the refactored source, build scripts, and a short README that documents dependencies, compile flag...

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...