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Open nowPosted 159 days ago

音频深度学习算法实习生（北京）

anker-in/campushirecn495 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now音频深度学习算法实习生（北京）anker-in/campushirecn · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. anker-in/campushirecn postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 159 days ago

anker-in/campushirecn median: 1 days open

The posting

安克实习生项目是面向正式校招岗位的人才培养与选拔通道。实习期间将按照校招标准进行系统的培养与综合评估，表现优秀者可直接获得校招转正机会，提前锁定正式校招席位。我们以严肃、长期的视角对待每一位实习生，也期待与你共同成长。 【你将参与】 1. 探索强化学习在 ANC、通话降噪、音效、语音识别等音频领域的应用 2. 设计与优化奖励函数、策略网络结构，提升音频处理效果与主观听感 3. 搭建和维护音频 RL 训练与评估流水线，包括仿真环境构建、数据采集、模型训练与性能评测 4. 参与端侧部署相关工作，包括模型量化、算力优化、实时性验证

必须项 1. 在读硕士/博士，音频、信号处理、计算机科学、自动化、电子信息等相关专业 2. 熟悉 PyTorch 框架，有完整的深度学习项目经验；了解 CNN/RNN/Transformer/Conformer 等主流网络架构 3. 了解音频信号处理基础（STFT、滤波器、声学特征等） 4. 掌握 RL 基本概念（MDP、策略梯度、Actor-Critic、PPO/SAC/DDPG 等），有至少一个 RL 相关项目或研究经验 5. 熟练使用 Python，熟悉 Linux 开发环境 加分项（满足任意一项即可） 1. 有音频处理相关研究经验（ANC、ENC、AEC、语音增强、音源分离等） 2. 有强化学习应用于连续控制/信号处理的经验 3. 了解端侧部署相关工作（量化、ARM NEON、DSP 等） 4. 有顶级会议/期刊论文发表（ICASSP、Interspeech、NeurIPS、ICML、ICLR 等）

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