米哈游图形算法研究员（深度学习布料仿真方向） - AI引擎
社招全职2年以上程序&技术类地点：上海状态：招聘
任职要求
1）计算机图形学/计算机视觉/高性能计算方向的硕士、博士研究生； 2）2 年以上物理模拟仿真或深度学习仿真相关领域研发经验，具备完整的算法落地经历； 3）对深度学习方法（MLP、CNN、GNN、Transformer 等）在仿真中的应用有实际经验； 4）熟练使用 PyTorch 或 TensorFlow，具备良好的代码规范、调试能力与较强的工程素养； 5）具备良好的沟通能力与团队协作精神，能够与算法、引擎、美术、TA 多角色高效配合。 加分项 1）熟悉主流布料 / 物理解算算法（PBD、XPBD、Mass-Spring、FEM、Projective Dynamics 等） 2…
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工作职责
1）深度学习布料仿真算法开发与工程落地 2）参与基于神经网络的布料 / 物理仿真算法的研究、实现与优化，并将算法稳定、高效地集成至引擎与实时管线中，服务于真实的项目需求。
包括英文材料
OpenCV+
https://learnopencv.com/getting-started-with-opencv/
At LearnOpenCV we are on a mission to educate the global workforce in computer vision and AI.
https://opencv.org/university/free-opencv-course/
This free OpenCV course will teach you how to manipulate images and videos, and detect objects and faces, among other exciting topics in just about 3 hours.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
CNN+
https://learnopencv.com/understanding-convolutional-neural-networks-cnn/
Convolutional Neural Network (CNN) forms the basis of computer vision and image processing.
[英文] CNN Explainer
https://poloclub.github.io/cnn-explainer/
Learn Convolutional Neural Network (CNN) in your browser!
https://www.deeplearningbook.org/contents/convnets.html
Convolutional networks(LeCun, 1989), also known as convolutional neuralnetworks, or CNNs, are a specialized kind of neural network for processing data.
https://www.youtube.com/watch?v=2xqkSUhmmXU
MIT Introduction to Deep Learning 6.S191: Lecture 3 Convolutional Neural Networks for Computer Vision
GNN+
https://distill.pub/2021/gnn-intro/
Neural networks have been adapted to leverage the structure and properties of graphs.
https://gnn.seas.upenn.edu/
Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs.
https://www.ibm.com/think/topics/graph-neural-network
Graph neural networks (GNNs) are a deep neural network architecture that is popular both in practical applications and cutting-edge machine learning research.
Transformer+
https://huggingface.co/learn/llm-course/en/chapter1/4
Breaking down how Large Language Models work, visualizing how data flows through.
https://poloclub.github.io/transformer-explainer/
An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.
https://www.youtube.com/watch?v=wjZofJX0v4M
Breaking down how Large Language Models work, visualizing how data flows through.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具，在灵活性、可读性和性能上都具备相当的优势，近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
Spring+
https://liaoxuefeng.com/books/java/spring/index.html
Spring是一个支持快速开发Java EE应用程序的框架。它提供了一系列底层容器和基础设施，并可以和大量常用的开源框架无缝集成，可以说是开发Java EE应用程序的必备。
https://spring.io/guides/gs/rest-service
https://spring.io/quickstart
Level up your Java code and explore what Spring can do for you.
CUDA+
https://developer.nvidia.com/blog/even-easier-introduction-cuda/
This post is a super simple introduction to CUDA, the popular parallel computing platform and programming model from NVIDIA.
https://www.youtube.com/watch?v=86FAWCzIe_4
Lean how to program with Nvidia CUDA and leverage GPUs for high-performance computing and deep learning.
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相关职位
实习程序&技术类
1）深度学习布料仿真算法开发与工程落地 2）参与基于神经网络的布料 / 物理仿真算法的研究、实现与优化，并将算法稳定、高效地集成至引擎与实时管线中，服务于真实的项目需求。
上海
实习程序&技术类
与团队紧密合作，追踪前沿算法，探索物理模拟与数据驱动结合方案，基于具体应用场景和数据，实现更优的动画表现效果。主要职责： 1.探索和开发物理模拟与深度学习结合的创新解决方案； 2.锚定特定物理动画效果，分析和定义问题，探索可行的实现路径； 3.与团队成员紧密合作，设计和实现相关方案，优化动画效果表现； 4.跟踪和研究业界与学界的物理模拟和深度学习前沿技术；
上海
校招程序&技术类
与团队紧密合作，追踪前沿算法，探索物理模拟与数据驱动结合方案，基于具体应用场景和数据，实现更优的动画表现效果。主要职责： 1.探索和开发物理模拟与深度学习结合的创新解决方案； 2.锚定特定物理动画效果，分析和定义问题，探索可行的实现路径； 3.与团队成员紧密合作，设计和实现相关方案，优化动画效果表现； 4.跟踪和研究业界与学界的物理模拟和深度学习前沿技术；
上海