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職位描述
本項目旨在開發(fā)一套基于脈沖波形庫與機器學(xué)習(xí)回歸分析的智能測量系統(tǒng)，用于溶液離子濃度和類型的自動識別與定量預(yù)測。
崗位主要負(fù)責(zé)脈沖庫的創(chuàng)建與可視化、PicoScope 設(shè)備控制、雙通道數(shù)據(jù)采集、信號特征提取、以及模型訓(xùn)練與性能評估。
崗位職責(zé)：
? 使用PicoScope 4000 系列實現(xiàn)化學(xué)溶液信號的采集與記錄；
? 參與脈沖波形庫的創(chuàng)建、優(yōu)化與可視化；
? 通過編程控制PicoScope實現(xiàn)自動化數(shù)據(jù)采集；
? 對實驗信號進行處理與特征提取，構(gòu)建機器學(xué)習(xí)數(shù)據(jù)集；
? 訓(xùn)練與優(yōu)化回歸模型（如 XGBoost 等）實現(xiàn)離子類型與濃度預(yù)測；
? 對結(jié)果進行可視化分析、性能評估，并撰寫項目技術(shù)報告。
? 學(xué)習(xí)電路搭建，電機控制，PCB相關(guān)知識和技能
任職條件：
1.電子、信號、通信、計算機或相關(guān)理工科專業(yè)碩士及以上學(xué)歷；
2.熟練掌握python編程及常用數(shù)據(jù)處理庫；
3.熟悉信號處理理論與方法（傅里葉變換、小波變換、濾波與特征分析）；
4.理解實驗儀器控制與數(shù)據(jù)采集（如AWG、PicoScope）；
5.熟悉機器學(xué)習(xí)回歸模型及模型優(yōu)化；
6.具備良好的科研習(xí)慣、數(shù)據(jù)分析及文檔撰寫能力；
7.具備較強的獨立思考、問題解決與團隊協(xié)作能力；
8.具備良好的英語書面和口語溝通能力，能夠使用英文進行工作交流。
加分項：
-有PicoScope使用經(jīng)驗或相關(guān)項目經(jīng)驗
-有嵌入式開發(fā)背景或相關(guān)項目經(jīng)驗（如ESP32、STM32）；
-熟悉Git/Github代碼管理
Signal Processing Engineer
Job Description
This project aims to develop an intelligent measurement system based on a pulse
waveform library and machine learning regression analysis, designed for
automatic identification and quantitative prediction of ion concentration and
type in chemical solutions.
The position primarily involves creating and visualizing the pulse library,
controlling the PicoScope device, conducting dual-channel data acquisition,
extracting signal features, and training and evaluating regression models.
Responsibilities:
? Use the PicoScope 4000 series to acquire and record signals from chemical
solutions;
? Participate in the creation, optimization, and visualization of the pulse
waveform library;
? Implement automated data acquisition through PicoScope programming control;
? Process and extract features from experimental signals to build machine
learning datasets;
? Train and optimize regression models (e.g., XGBoost) for ion type and
concentration prediction;
? Perform result visualization, performance evaluation, and prepare technical
project reports.
Qualifications:
1. Master’s degree or above in Electronics, Signal Processing, Communications, Computer Science, or other related engineering fields;
2. Proficiency in Python programming and common data processing libraries;
3. Solidunderstanding of signal processing theories and methods (Fourier Transform, Wavelet Transform, filtering, and feature analysis);
4. Familiarity with experimental instrument control and data acquisition (e.g., AWG, PicoScope);
5. Knowledge of machine learning regression models and model optimization techniques;
6. Strong research habits, data analysis, and technical documentation skills;
7. Excellent independent thinking, problem-solving, and teamwork abilities;
8. Good written and spoken English communication skills for technical collaboration.
Preferred Qualifications:
? Experience using PicoScope or related projects;
? Background or experience in embedded development (e.g., ESP32, STM32);
? Familiarity with Git/GitHub for code management.