CakeResume 找人才
進階搜尋
On
4 到 6 年
6 到 10 年
10 到 15 年
15 年以上
Taichung City, Taiwan
Data engineer @H2 Inc.2021 ~ 現在
・
AI engineer, ML engineer, data scientist
三個月內
on validation dataset. Accuracy 達到 93%, 已與醫師合作發表醫學 paper 3. Pathology案件 -建立 two stage segmentation model, stage one segmentation model 達到86% IOU, stage two segmentation model 達到 94% custom dice coefficient 學歷 清華大學 動力機械工程學系技能 Software AWS Airflow Dagster Docker Git Flask Pytorch Tensorflow DVC Languages Python SQL Bash-Shell script C/C++ Javascript Technical Skill Data Modeling Custom ETL Development Data Analysis System Resource Analysis AWS IaaS
就職中
・
目前會考慮了解新的機會
全職 / 對遠端工作有興趣
4 到 6 年
清華大學
・
動力機械工程學系
Senior AI Research/Engineer (part-time) @NeuroBonic Inc.2022 ~ 2023
・
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
一個月內
就職中
・
目前會考慮了解新的機會
全職 / 對遠端工作有興趣
4 到 6 年
National Yang Ming Chiao Tung University
・
Computer Science
資深經理 @緯創資通2021 ~ 現在
・
Technical Manager
半年內
林昭源 (Leo Lin) 1. Two years of management experience. 2. More than 10 years of computer vision and deep learning/software architecture development experience. 3. Programming experience using python. 4. Good paper reading ability and practical ability 5. Familiar with computer vision, deep learning (CNN, Resnet, densnet, GAN), object detection (Yolo series, RCNN series), segmentation models (UNet, DensUNet). 6. Experience in semi-supervised or unsupervised learning (pesudo labeling, Voxmorph model). 7. Experience with Docker, Git, Jenkins DevOps. Education: National Taiwan University of Science and
就職中
全職 / 對遠端工作有興趣
10 到 15 年
National Taiwan University of Science and Technology
・
Master's degree Computer Science and Information Engineering最輕量、快速的招募方案，數百家企業的選擇
搜尋履歷，主動聯繫求職者，提升招募效率。
- 瀏覽所有搜尋結果
- 每日可無限次數開啟陌生對話
- 搜尋僅開放付費企業檢視的履歷
- 檢視使用者信箱 & 電話
搜尋技巧
1
嘗試搜尋最精準的關鍵字組合
資深 後端 php laravel
2
將須完全符合的字詞放在雙引號中
"社群行銷"
3
在不想搜尋到的字詞前面加上減號，如果想濾掉中文字，需搭配雙引號使用 (-"人資")
UI designer -UX
職場能力評價定義
專業技能
該領域中具備哪些專業能力（例如熟悉 SEO 操作，且會使用相關工具）。
問題解決能力
能洞察、分析問題，並擬定方案有效解決問題。
變通能力
遇到突發事件能冷靜應對，並隨時調整專案、客戶、技術的相對優先序。
溝通能力
有效傳達個人想法，且願意傾聽他人意見並給予反饋。
時間管理能力
了解工作項目的優先順序，有效運用時間，準時完成工作內容。
團隊合作能力
具有向心力與團隊責任感，願意傾聽他人意見並主動溝通協調。
領導力
專注於團隊發展，有效引領團隊採取行動，達成共同目標。
專業背景
目前狀態
待業中
求職階段
專業
軟體工程師, Python 開發人員, 機器學習工程師
產業
人工智慧 / 機器學習, 軟體, 資訊服務
工作年資
小於 1 年
管理經歷
無
技能
語言能力
求職偏好
希望獲得的職位
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
預期工作模式
全職
期望的工作地點
台灣台北, 台灣新北市, 台灣桃園, 台灣新竹, 台灣苗栗, 台灣台中
遠端工作意願
對遠端工作有興趣
接案服務
否
學歷
學校
國立台灣科技大學
主修科系
AI
莊才賢,Cai-Xian(John) Zhuang
[email protected]
+886-983-865-151
About Me
- I graduated from the master of AI group in National Taiwan University of Science and Technology. I like to use AI models to solve some problems that especially focus on computer vision and some image processing.
- I like to challenge the impossible, and I am curious about new knowledge.
- I am looking for a software engineer for a company where I can apply my abilities to help my team to achieve what clients want.
Education & Experience
National Taiwan University of Science and Technology, M.S., Electrical Engineering- AI, Jul. 2019 - Sep. 2021
- Courses included: Machine Learning, Algorithm, Deep Learning, Image and Video Processing
- Research: Video-Based Blood Pressure Estimation Using Two-Step Learning in 3D-CNN
- Predicting ages by faces achieves only 4.2 ages error.
- Predicting gender by faces achieves approximately 98% accuracy.
- Subjects included: Program Designing, Control System, Deep Learning Application Machine Learning
- Eighth place in the whole department
Skills
Language
- Python
- C++
- Arduino
- MATLAB
IDE/Editor Tool
- Jupyter Notebook
- Spyder3
- PyCharm
- Code::Blocks
- Visual Studio
- GitHub
Cloud Service & Database
- Google Colab
- MySQL
Other Knowledge
- AI (Artificial Intelligence)
- Image Preprocessing
- Algorithm
Projects
1.Video-Based Blood Pressure Estimation Using Two-Step Learning in 3D-CNN
We used only face frames to estimate blood pressure with a normal webcam. We proposed two training skills and a two-step learning method to reduce the loss. We finally achieve 6.95/6.53 mmHg (MAEs for SBP/DBP) and 7.50/6.29 (standard deviation for SBP/DBP) which pass the criteria of AAMI.
- Language : Python
- API : Pytorch, Matplotlib, Pandas, cv2, PIL
2.Voice to Sign Language Translation System
Customer says some words. Microphone will collect them. Computer will show the Sign Language films to the hearing-impaired people.
- Language : Python
- API : SpeechRecognition, jieba, Tkinter, cv2
3.The Simpsons Characters Recognition Challenge
We used Simpson dataset and CNN architecture as the model of training and prediction.
- Language : Python
- API : Keras, Matplotlib, Pandas, sklearn, cv2
4.Score Your Face
We used SCUT-FBP5500_v2 dataset and ResNet50 architecture as the model of training and score people’s faces.
- Language : Python
- API : Keras, Matplotlib, Pandas, sklearn, cv2
5.Smart Parking Lot
We combined Arduino and Python to build Smart Parking Lot. We used Arduino to be our warning light, sensors, motors and management system. Using Python to detect and identify license plate.
- Language : Arduino, Python
- API : Keras, Matplotlib, Pandas, sklearn, cv2
Contests
- Participate in 2019 11th IT help ironman competition (AI & Data)
- 2018 NTUT Senior Project Contest - The High Distinction Award
- Participate in 2017 TEL Robot Combat final contest(東京威力科創機器人大賽)
莊才賢,Cai-Xian(John) Zhuang
[email protected]
+886-983-865-151
About Me
- I graduated from the master of AI group in National Taiwan University of Science and Technology. I like to use AI models to solve some problems that especially focus on computer vision and some image processing.
- I like to challenge the impossible, and I am curious about new knowledge.
- I am looking for a software engineer for a company where I can apply my abilities to help my team to achieve what clients want.
Education & Experience
National Taiwan University of Science and Technology, M.S., Electrical Engineering- AI, Jul. 2019 - Sep. 2021
- Courses included: Machine Learning, Algorithm, Deep Learning, Image and Video Processing
- Research: Video-Based Blood Pressure Estimation Using Two-Step Learning in 3D-CNN
- Predicting ages by faces achieves only 4.2 ages error.
- Predicting gender by faces achieves approximately 98% accuracy.
- Subjects included: Program Designing, Control System, Deep Learning Application Machine Learning
- Eighth place in the whole department
Skills
Language
- Python
- C++
- Arduino
- MATLAB
IDE/Editor Tool
- Jupyter Notebook
- Spyder3
- PyCharm
- Code::Blocks
- Visual Studio
- GitHub
Cloud Service & Database
- Google Colab
- MySQL
Other Knowledge
- AI (Artificial Intelligence)
- Image Preprocessing
- Algorithm
Projects
1.Video-Based Blood Pressure Estimation Using Two-Step Learning in 3D-CNN
We used only face frames to estimate blood pressure with a normal webcam. We proposed two training skills and a two-step learning method to reduce the loss. We finally achieve 6.95/6.53 mmHg (MAEs for SBP/DBP) and 7.50/6.29 (standard deviation for SBP/DBP) which pass the criteria of AAMI.
- Language : Python
- API : Pytorch, Matplotlib, Pandas, cv2, PIL
2.Voice to Sign Language Translation System
Customer says some words. Microphone will collect them. Computer will show the Sign Language films to the hearing-impaired people.
- Language : Python
- API : SpeechRecognition, jieba, Tkinter, cv2
3.The Simpsons Characters Recognition Challenge
We used Simpson dataset and CNN architecture as the model of training and prediction.
- Language : Python
- API : Keras, Matplotlib, Pandas, sklearn, cv2
4.Score Your Face
We used SCUT-FBP5500_v2 dataset and ResNet50 architecture as the model of training and score people’s faces.
- Language : Python
- API : Keras, Matplotlib, Pandas, sklearn, cv2
5.Smart Parking Lot
We combined Arduino and Python to build Smart Parking Lot. We used Arduino to be our warning light, sensors, motors and management system. Using Python to detect and identify license plate.
- Language : Arduino, Python
- API : Keras, Matplotlib, Pandas, sklearn, cv2
Contests
- Participate in 2019 11th IT help ironman competition (AI & Data)
- 2018 NTUT Senior Project Contest - The High Distinction Award
- Participate in 2017 TEL Robot Combat final contest(東京威力科創機器人大賽)