CakeResume 找人才
进阶搜寻
On
4 到 6 年
6 到 10 年
10 到 15 年
15 年以上
National Taiwan University
人工智慧工程師 @玉山銀行智能金融處 (Intelligent Finance Dept., E-SUN Bank )2020 ~ 现在
・
Machine Learning Researcher / Engineer
一個月內
就职中
全职 / 对远端工作有兴趣
4 到 6 年
National Taiwan University
・
Graduate Institute of Communication Engineering
NAND Mechanism RD, Machine Learning Engineer @PHISON2015 ~ 现在
・
Machine Learning Engineer / Data Science Engineer
超過一年
Kled Chen Assistant Manager (Now) Software Engineer (9 years) Algorithm Developer (6 years) Machine Learning Engineer (3 years) Physics Master Financial Researcher [email protected] Skills Power Point / Excel / Office C++ / C / Python / Git Algorithm Development / Data analyse Machine Learning / Neuron network / Tensorflow / Keras / Autoencoder / CNN Physics 學歷 NATIONAL TAIWAN UNIVERSITY, Physics Master, 2008 ~ 2010 ‧ Magnetic Quantum Confinement and Interaction of Co Nanoislands Resolved by Spin-Polarized Scanning Tunneling Microscopy and Spectroscopy ‧ 2010 Physics Annual Meeting of the Physics Best Paper in Magnetics Material
全职 / 对远端工作有兴趣
6 到 10 年
NATIONAL TAIWAN UNIVERSITY
・
Physics Master
AI enigneer @, Coretronic Inc.2019 ~ 现在
・
超過一年
Conduct biological experiments with different type of optical spectroscopy , including total-internal-reflection spectroscopy TIRF , fluorescence spectroscopy , laser scanning confocal microscopy. AugJunMental counsultant Pingtong 298 Mechanized Infantry 學歷National Taiwan University Master Applied PhysicsNational Taiwan University Bachelor Physics 技能 Python: Scientific computing: numpy, scipy Web framework: flask, Django Machine-learning: scikit-learn, keras, gensim, tensorflow Nework investigation: networkx Javascript: cytoscape.js, highcharts.js, D3.js Database: mongodb, SQL, Postgres 語言 Mandarin. Native Fluency Taiwanese Native Fluency English. Professional Fluency(TOEFL score: reading(27), listening(24), speaking(18), writing(22)) Spanish Basic
全职 / 对远端工作有兴趣
6 到 10 年
National Taiwan University
・
Master Applied Physics就职中
・
目前没有兴趣寻找新的机会
全职 / 暂不考虑远端工作
4 到 6 年
National Taiwan University
・
EconomicsJr. Programmer @德義資訊股份有限公司2013 ~ 2015
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Developer Team Leader, Architect, FullStack Developer
超過一年
就职中
全职 / 对远端工作有兴趣
6 到 10 年
National Taiwan University
・
Bachelor of Bio-Industrial Mechatronics Engineering最轻量、快速的招募方案，数百家企业的选择
搜寻简历，主动联系求职者，提升招募效率。
- 浏览所有搜寻结果
- 每日可无限次数开启陌生对话
- 搜尋僅開放付費企業檢視的简历
- 检视使用者信箱 & 电话
搜寻技巧
1
Search a precise keyword combination
senior backend php
2
Use quotes to search for an exact phrase
"business development"
3
Use the minus sign to eliminate results containing certain words
UI designer -UX
职场能力评价定义
专业技能
该领域中具备哪些专业能力（例如熟悉 SEO 操作，且会使用相关工具）。
问题解决能力
能洞察、分析问题，并拟定方案有效解决问题。
变通能力
遇到突发事件能冷静应对，并随时调整专案、客户、技术的相对优先序。
沟通能力
有效传达个人想法，且愿意倾听他人意见并给予反馈。
时间管理能力
了解工作项目的优先顺序，有效运用时间，准时完成工作内容。
团队合作能力
具有向心力与团队责任感，愿意倾听他人意见并主动沟通协调。
领导力
专注于团队发展，有效引领团队采取行动，达成共同目标。
专业背景
目前状态
就职中
求职阶段
专业
软体工程师, 研发, 机器学习工程师
产业
制造, 人工智能 / 机器学习, 软件
工作年资
1 到 2 年
管理经历
无
技能
语言能力
Chinese
・
母语或双语
English
・
中阶
Japanese
・
初阶
求职偏好
希望获得的职位
機器學習、資料科學工程師
预期工作模式
全职
期望的工作地点
Taiwan, 台灣, Taipei, 台灣
远端工作意愿
对远端工作有兴趣
接案服务
是，我利用业余时间接案
学历
学校
National Taiwan University
主修科系
Civil Engineering
Zhao-Yang Zhuang (莊昭陽)
Experience with computer vision, deep learning. I am very passionate about design a novel DL model architecture for real world problems and hope to participate in more types of applications.
ML engineer Data scientist
[email protected] https://www.linkedin.com/in/rogan-zhuang/
Educations
National Taiwan University
Civil Engineering, Computer-Aided Engineering Group (M.S. degree)
(2018 ~ 2020)
Master Thesis:
Deep-learning method assisted crane for sway prediction: Recurrent Kalman Network.
Work Experiences
(Automation Services Co., LTD.) (AI/CV Engineer)
(Feb, 2021 - Oct, 2021)
- The main developer of AI/CV algorithm.
- Compiled the AI/CV part of automatic fabric inspection machine alone from scratch.
Research Experiences
Deep-learning method assisted crane for sway prediction : Recurrent Kalman Network
(Dec, 2019 - Jun, 2020)
Robotic LAB, NTU & 祐彬 Construction Co., Ltd
- Predicting the future position of a payload by RKN to assist in the automation of crane.
- The state of the payload system can be inferred by the combined information between the image pixels.
Tensorflow(Keras) OpenCV Python C/C++ Deep Learning
The second generation of automatic fabric inspection machine
(Feb, 2021 - Apr, 2021)
Automation Services Co., LTD.
- Detecting the defects on the textile by deep learning that efficiency is at least ten times of manual visual inspection.
- Calculate the area and location of defects by semantic segmentation.
- The prototype machine can detect the width of about two meters with eight cameras.
C# Deep Learning
The third generation of automatic fabric inspection machine
(May, 2021 - Oct, 2021)
Automation Services Co., LTD.
- Based on the second-generation inspection machine, all of the features are upgraded, such as image grabbing system, camera calibration system, deep learning framework, thread optimization, etc.
- This mass production machine is online.
- Not implemented using Tensorflow or Pytorch.
Key achievement: 100% detection of defects required by customers, 36x8 frames per second detection speed.
C# Deep Learning Cameras System Multithread Programming
Side Projects
- Image automatic labeling: CNN-base object tracking by Siam-Net. [labeling 90x faster than manual]
- Document layout analyzing: locate file elements (titles, text, pictures, etc.) by image segmentation.
- HousePrice-prediction: Predict the HousePrice in Feature Engineering & Advanced Regression. [Kaggle top 0.04%]
- Flowers classification: Distributed training a CNN to classify 104 kinds of flower pictures. [Kaggle top 0.09%]
Zhao-Yang Zhuang (莊昭陽)
Experience with computer vision, deep learning. I am very passionate about design a novel DL model architecture for real world problems and hope to participate in more types of applications.
ML engineer Data scientist
[email protected] https://www.linkedin.com/in/rogan-zhuang/
Educations
National Taiwan University
Civil Engineering, Computer-Aided Engineering Group (M.S. degree)
(2018 ~ 2020)
Master Thesis:
Deep-learning method assisted crane for sway prediction: Recurrent Kalman Network.
Work Experiences
(Automation Services Co., LTD.) (AI/CV Engineer)
(Feb, 2021 - Oct, 2021)
- The main developer of AI/CV algorithm.
- Compiled the AI/CV part of automatic fabric inspection machine alone from scratch.
Research Experiences
Deep-learning method assisted crane for sway prediction : Recurrent Kalman Network
(Dec, 2019 - Jun, 2020)
Robotic LAB, NTU & 祐彬 Construction Co., Ltd
- Predicting the future position of a payload by RKN to assist in the automation of crane.
- The state of the payload system can be inferred by the combined information between the image pixels.
Tensorflow(Keras) OpenCV Python C/C++ Deep Learning
The second generation of automatic fabric inspection machine
(Feb, 2021 - Apr, 2021)
Automation Services Co., LTD.
- Detecting the defects on the textile by deep learning that efficiency is at least ten times of manual visual inspection.
- Calculate the area and location of defects by semantic segmentation.
- The prototype machine can detect the width of about two meters with eight cameras.
C# Deep Learning
The third generation of automatic fabric inspection machine
(May, 2021 - Oct, 2021)
Automation Services Co., LTD.
- Based on the second-generation inspection machine, all of the features are upgraded, such as image grabbing system, camera calibration system, deep learning framework, thread optimization, etc.
- This mass production machine is online.
- Not implemented using Tensorflow or Pytorch.
Key achievement: 100% detection of defects required by customers, 36x8 frames per second detection speed.
C# Deep Learning Cameras System Multithread Programming
Side Projects
- Image automatic labeling: CNN-base object tracking by Siam-Net. [labeling 90x faster than manual]
- Document layout analyzing: locate file elements (titles, text, pictures, etc.) by image segmentation.
- HousePrice-prediction: Predict the HousePrice in Feature Engineering & Advanced Regression. [Kaggle top 0.04%]
- Flowers classification: Distributed training a CNN to classify 104 kinds of flower pictures. [Kaggle top 0.09%]