Senior MATLAB Developer Needed: Custom Data Pre-Processing & Statistical Analysis GUI

We are seeking an experienced MATLAB developer to build a clean, custom Graphical User Interface (GUI) using MATLAB ( App Designer preferred) for processing, visualizing, and analyzing continuous trial/time-series data. Core Responsibilities: 1. Data Ingestion & Visualization: a. Import and parse structured .mat datasets (multi-condition, multi-subject, and multi-trial time-series data). b. Render interactive 2D plots for raw and processed signal trajectories. 2. Data Pre-Processing Pipeline: a. Implement modular pre-processing features including event detection (e.g., automatically identifying trajectory start/end indices), signal smoothing, and temporal alignment/normalization across trials. b. Provide dynamic control inputs allowing users to configure at least 5 adjustable pre-processing parameters (e.g., filter cutoff frequencies, smoothing window sizes, threshold cutoffs, padding). 3. Feature Extraction & Statistical Comparison: a. Build selection controls enabling side-by-side comparison of user-selected conditions across custom-extracted features (at least 2 simultaneous metrics). b. Integrate custom statistical test parameters and inference testing to evaluate differences between experimental conditions. c. Compute effect sizes and custom variance-ratio measures to quantify condition-based differences relative to inter-subject variability. 4. Reporting & UI Outputs: a. Display high-resolution graphical plots and numeric analytical summaries directly within the interface. b. Implement clear visual indicators for test statistics and confidence measures. Deliverables: 1. Complete, well-documented MATLAB source code (.mlapp, .m, and/or .fig). 2. A technical write-up (PDF) explaining the software architecture, underlying algorithmic choices, signal processing logic, and mathematical rationale behind the GUI implementation. Technical Requirements & Qualifications: 1. Strong proficiency in MATLAB (App Designer, Signal Processing Toolbox, and Statistics and Machine Learning Toolbox). 2. Solid foundation in time-series data analysis, digital signal filtering, and inferential statistics (effect sizes, hypothesis testing). 3. Ability to write clean, modular, and well-commented code.