1、工作内容(Job description):
方向一:
1).进行文献回顾并实现sota视觉语言模型。
2).设计并进行实验以评估不同模型的性能。分析研究结果并提出改进建议。
3).写高质量的报告来记录和交流你的发现和贡献。表现出与他人联系和协作的能力。
1).Conduct literature review and implement sota vision-language models.
2).Design and conduct experiments to evaluate the performance of different models. Anlyze the results and propose improvements for future research.
3).Write high-quality reports to document and communicate your findings and contributions.
方向二:
1).计算机视觉领域中与人类相关的sota模型的发展。
2).设计评价方案,分析实验结果。
3). 高质量的技术总结和报告。
1).Development of human-related sota models in the field of computer vision.
2).Design evaluation plan and analyze experimental results.
3). High-quality technical summary and reporting.
2、关健技能要求(Key Technologies Involved):
人工智能研究,计算机视觉模型。
AI research,computer vision models.
3、职位要求(Requirements)
1).目前正在攻读计算机视觉,机器学习或相关领域的学士或以上学位。
2). 熟悉深度学习,计算机视觉。
3). 精通python和至少一个以下框架:pytorh, tensorflow。有C/ c++经验者优先。
4).表现出与他人联系和协作的能力。
5).理想的候选人还将在深度学习相关的学术,研究或证明经验。
1).Currently pursuing a bachelor or above degree in computer vision, machie learning, or related field.
2). Familiar with deep learning, computer vision.
3). Proficient in python and at least one of the following frameworks: pytorh, tensorflow. Experience in C/C++ is plus.
4).Demonstrated ability to connect and collaborate with others.
5).Ideal candidates will also have academic, research, or proven experience in deep learning related.
2、关健技能要求(Key Technologies Involved):
深度学习(Deep Learning)。
3、职位要求(Requirements)
1)、本科及以上学历,电子、计算机、控制等相关专业;
2)、熟练使用c++/python等开发语言,熟练使用tensorflow、pytorch等培训框架;
3)、对深度学习、计算机视觉、机器学习等理论知识有较深入的了解。
4)、熟练掌握基本的计算机知识,数据结构和算法;
5)、 有很强的自我驱动精神,愿意解决具有挑战性的问题;
6)、 附加项目:
A.有深度学习相关项目经验者优先。
B.对于开源代码贡献者来说是一个很大的加分项。
C.那些发表过论文/专利的人更有优势。
1). Bachelor degree or above, major in electronics, computer, control and other related majors.
2). Proficiency in development languages such as c++/python, and be proficient in using training frameworks such as tensorflow or pytorch.
3). Have a deep understanding of deep learning, computer vision, machine learning and other theoretical knowledge.
4). Proficiency in basic computer knowledge, data structures and algorithms.
5). Have a strong self-driven spirit and willingness to solve challenging problems.
6). Bonus items:
a. Deep learning related project experience is a plus.
b. big plus for open source code contributors.
c. big plus for those who have published papers/patents.