Job Description

- Research on case studies and research papers regarding process/equipment/battery anomaly detection - Identification of data sources (PLCs, sensors, DBs, etc.) and security requirements - Development of data preprocessing modules (resampling, missing values, filtering, feature extraction, QA metrics) - Development of data operations platform UI/dashboards and server integration - Log and error handling for the collection→cleaning→storage→analysis workflow - Time-series visualization and classification/analysis of anomaly patterns - Development of anomaly detection algorithms and real-world verification (Autoencoder/LSTM-AE/TCN, etc.)

Qualifications

- Current students or graduates in science and engineering (Computer Science, Electrical & Electronic Engineering, Mechanical Engineering, Data Science, etc.) - Basic Python proficiency (pandas, numpy) - Understanding of basic machine learning theory - Experience with Git collaboration

Preferred

- Experience with time-series data processing - Experience using PyTorch / TensorFlow - Experience implementing anomaly detection models (Autoencoder, LSTM, TCN, etc.) - Experience using data visualization tools (Plotly, Grafana, etc.) - Experience developing web servers (FastAPI, Flask, etc.) - Understanding of industrial communication protocols (PLC, Modbus, OPC-UA, etc.) - Experience with time-series databases (InfluxDB, TimescaleDB, etc.)

Preferred Visas

Benefits

Cover Letter

Required

Portfolio

Optional