仕事の情報

業界

Communications

市区町村

Tokyo, Japan

都道府県

Tokyo

郵便番号

仕事の詳細

Company Overview

Infostellar is a pioneering Japanese company revolutionizing satellite communications through its innovative ground station sharing platform, StellarStation. Our mission is to democratize access to satellite data by providing a global network of ground stations, enabling seamless communication between satellite operators and their spacecraft. By leveraging cutting-edge technology, we empower the New Space industry to achieve greater efficiency and scalability in satellite operations. As part of our ongoing research and development efforts, we are seeking a motivated intern to join our team and contribute to a groundbreaking project involving signal detection and generation for satellite communications.

Internship Overview

We are looking for a highly motivated and detail-oriented intern to assist in a project focused on the automatic detection of signals from IQ (In-phase and Quadrature) data files or the generation of synthetic signals for training machine learning algorithms. The intern will work closely with our team of engineers and data scientists to design, implement, and test solutions using tools such as GNURadio and MATLAB. This is an excellent opportunity to gain hands-on experience in signal processing, software-defined radio, and machine learning while contributing to real-world applications.

Key Responsibilities

• Signal Detection from IQ Files:

• • Use GNURadio or MATLAB to preprocess, analyze, and extract features from IQ data files.

• Develop and implement algorithms to automatically detect and classify signals of interest.

• Test and validate detection algorithms against real-world and synthetic datasets.

• Signal Generation for Machine Learning:

• • Use GNURadio or MATLAB to generate synthetic signals with varying parameters (e.g., modulation types, noise levels, interference).

• Create labeled datasets of synthetic signals to train machine learning models for signal detection and classification.

• Collaborate with the machine learning team to integrate generated datasets into training pipelines.

• Algorithm Development and Optimization:

• • Research and propose signal processing techniques to improve detection accuracy and efficiency.

• Optimize algorithms for performance, scalability, and real-time processing.

• Documentation and Reporting:

• • Document code, methodologies, and experimental results in a clear and organized manner.

• Present findings and progress to the team during regular meetings.

• Collaboration:

• • Work closely with engineers, data scientists, and other interns to troubleshoot issues and brainstorm solutions.

• Participate in code reviews and contribute to maintaining high-quality code standards.

Learning Opportunities

During this internship, you will:

• Gain hands-on experience with state-of-the-art signal processing and SDR tools.

• Learn how to design and implement algorithms for real-world signal detection and generation.

• Develop skills in integrating signal processing with machine learning workflows.

• Receive mentorship from experienced engineers and researchers in the field.

Internship Details

• Duration: 3 months to 6 months
• Location: Remote or Hybrid

要件

Required Qualifications

• Currently pursuing a Bachelor's, Master's, or PhD degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field.

• Familiarity with signal processing concepts (e.g., modulation, filtering, Fourier transforms, etc.).

• Experience with at least one of the following tools: GNURadio, MATLAB, or similar signal processing frameworks.

• Basic programming skills in Python, MATLAB, or C/C++.

• Strong problem-solving skills and attention to detail.

• Ability to work independently and as part of a collaborative team.

• Excellent written and verbal communication skills.

Preferred Qualifications

• Experience with software-defined radio (SDR) platforms and tools (e.g., GNURadio, SDR hardware such as USRP).

• Knowledge of machine learning concepts and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

• Familiarity with IQ data formats and signal analysis techniques.

• Previous coursework or projects in digital communications, wireless systems, or radar signal processing.

• Experience with version control systems (e.g., Git).