仿真AI应用实习生
招聘时间
即日起 至 2026-07-02
招聘单位
博西家电 B/S/H/
所在地区
南京
工作地点
尧新大道208号
学历要求
本科
实习时长
每周4天，每天6小时
职责描述
岗位职责：
1. Web-Based Development of Simulation Fast-Calculation: refactor existing Python calculation scripts into interactive HTML-based web applications, and develop front-end pages to support parameter input, calculation execution, and result display/仿真快速计算工具网页化开发：将现有python计算脚本整理并封装为可交互的HTML网页，负责前端页面开发、实现参数输入、计算触发及结果展示等基础功能。
2. Participate in the design and implementation of AI-based control algorithms for refrigerators, including but not limited to: Temperature control optimization using machine learning or reinforcement learning, energy consumption prediction models, and intelligent scheduling of actuator/参与冰箱AI控制算法的设计与实现，包括但不限于：基于机器学习/强化学习的温控策略优化，冰箱能耗预测模型构建，执行器的智能调度策略。
3. Validate and iteratively optimize control strategies based on simulation or experimental data/在仿真或实验数据基础上，对控制策略进行验证与迭代优化。
教育背景：
本科或研究生，计算机科学，软件工程，AI等相关专业
特定专业知识：
1. Proficient in Python programming with good coding practices/熟练掌握 Python 编程语言，具备良好的代码规范意识。
2. Familiar with at least one machine learning framework: TensorFlow/PyTorch/Scikit-learn/熟悉至少一种机器学习框架：TensorFlow/PyTorch/Scikit-learn (满足一种即可）。
3. Basic understanding of control theory (e.g., feedback control, PID, state variables)/了解基本的控制理论（如反馈控制、PID、状态变量等）。
4. General knowledge of intelligent optimization algorithms, such as reinforcement learning (Q-learning), and genetic algorithms or other heuristic optimization methods/对智能优化算法有一定认知，如：强化学习（Q-learning),遗传算法或其他启发式优化算法。
1. Web-Based Development of Simulation Fast-Calculation: refactor existing Python calculation scripts into interactive HTML-based web applications, and develop front-end pages to support parameter input, calculation execution, and result display/仿真快速计算工具网页化开发：将现有python计算脚本整理并封装为可交互的HTML网页，负责前端页面开发、实现参数输入、计算触发及结果展示等基础功能。
2. Participate in the design and implementation of AI-based control algorithms for refrigerators, including but not limited to: Temperature control optimization using machine learning or reinforcement learning, energy consumption prediction models, and intelligent scheduling of actuator/参与冰箱AI控制算法的设计与实现，包括但不限于：基于机器学习/强化学习的温控策略优化，冰箱能耗预测模型构建，执行器的智能调度策略。
3. Validate and iteratively optimize control strategies based on simulation or experimental data/在仿真或实验数据基础上，对控制策略进行验证与迭代优化。
教育背景：
本科或研究生，计算机科学，软件工程，AI等相关专业
特定专业知识：
1. Proficient in Python programming with good coding practices/熟练掌握 Python 编程语言，具备良好的代码规范意识。
2. Familiar with at least one machine learning framework: TensorFlow/PyTorch/Scikit-learn/熟悉至少一种机器学习框架：TensorFlow/PyTorch/Scikit-learn (满足一种即可）。
3. Basic understanding of control theory (e.g., feedback control, PID, state variables)/了解基本的控制理论（如反馈控制、PID、状态变量等）。
4. General knowledge of intelligent optimization algorithms, such as reinforcement learning (Q-learning), and genetic algorithms or other heuristic optimization methods/对智能优化算法有一定认知，如：强化学习（Q-learning),遗传算法或其他启发式优化算法。
