«فرم دستیاری درس شبکه‌های کامپیوتری، گروه دکتر صادق‌زاده»
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Google Docs
فرم دستیاری شبکه‌های کامپیوتری (دکتر صادق‌زاده)
در صورت هرگونه سوال ایمیلی به [email protected] ارسال نمایید
«فرم دستیاری درس مبانی و کاربردهای بلاکچین (دکتر صادق‌زاده)»
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Google Docs
فرم دستیاری مبانی و کاربردهای بلاکچین (دکتر صادق‌زاده)
در صورت هرگونه سوال ایمیلی به [email protected] ارسال نمایید
«دستیاری درس امنیت و حریم خصوصی در یادگیری ماشین»
ما برای ترم پاییز 1404 به دنبال دستیار برای درس امنیت و حریم خصوصی در یادگیری ماشین هستیم. اگر در هر یک از زمینه های زیر تجربه ی عملی و یا تئوری دارید و علاقه مند به همکاری در مطالب درس هستید فرمی که در انتهای این پیام گذاشته شده است را تکمیل فرمایید
Deep Learning
Adversarial Attacks and Defenses
Data Poisoning and Model Extraction
Membership Inference
Federated Learning and Differential Privacy
Generative Models Security
همچنین برای دیدن مطالب درس میتوانید به آدرس سایت ترم های گذشته درس https://spml2024.github.io مراجعه کنید
لینک فرم همکاری - لینک
ما برای ترم پاییز 1404 به دنبال دستیار برای درس امنیت و حریم خصوصی در یادگیری ماشین هستیم. اگر در هر یک از زمینه های زیر تجربه ی عملی و یا تئوری دارید و علاقه مند به همکاری در مطالب درس هستید فرمی که در انتهای این پیام گذاشته شده است را تکمیل فرمایید
Deep Learning
Adversarial Attacks and Defenses
Data Poisoning and Model Extraction
Membership Inference
Federated Learning and Differential Privacy
Generative Models Security
همچنین برای دیدن مطالب درس میتوانید به آدرس سایت ترم های گذشته درس https://spml2024.github.io مراجعه کنید
لینک فرم همکاری - لینک
📢 Research Assistant Positions Available
The Trustworthy and Secure Artificial Intelligence Lab (TSAIL) and the Robust and Interpretable Machine Learning (RIML) Lab at the Computer Engineering Department of Sharif University of Technology are seeking highly motivated and talented research assistants to join our team. This collaborative project is jointly supervised by Dr. Sadeghzadeh and Dr. Rohban.
🔍 Position Overview
We are working on cutting-edge research in the field of generative models, with a focus on robustness, interpretability, and trustworthiness. As a research assistant, you will contribute to impactful projects at the intersection of theory and real-world applications.
🧠 Required Qualifications
- Solid background in machine learning, artificial intelligence, and generative models
- Hands-on experience with generative models and their practical applications
- Proficiency in Python and frameworks such as PyTorch
- Strong communication skills and the ability to work well in a collaborative research environment
📝 How to Apply
If you are interested in joining our team, please complete the application form and upload your CV using the following link:
👉 Application Form
📚 Suggested Background Reading
To better understand the context of our research, we recommend reviewing the following papers:
1. http://arxiv.org/abs/2410.15618
2. http://arxiv.org/abs/2305.10120
We look forward to your application!
The Trustworthy and Secure Artificial Intelligence Lab (TSAIL) and the Robust and Interpretable Machine Learning (RIML) Lab at the Computer Engineering Department of Sharif University of Technology are seeking highly motivated and talented research assistants to join our team. This collaborative project is jointly supervised by Dr. Sadeghzadeh and Dr. Rohban.
🔍 Position Overview
We are working on cutting-edge research in the field of generative models, with a focus on robustness, interpretability, and trustworthiness. As a research assistant, you will contribute to impactful projects at the intersection of theory and real-world applications.
🧠 Required Qualifications
- Solid background in machine learning, artificial intelligence, and generative models
- Hands-on experience with generative models and their practical applications
- Proficiency in Python and frameworks such as PyTorch
- Strong communication skills and the ability to work well in a collaborative research environment
📝 How to Apply
If you are interested in joining our team, please complete the application form and upload your CV using the following link:
👉 Application Form
📚 Suggested Background Reading
To better understand the context of our research, we recommend reviewing the following papers:
1. http://arxiv.org/abs/2410.15618
2. http://arxiv.org/abs/2305.10120
We look forward to your application!
arXiv.org
Erasing Undesirable Concepts in Diffusion Models with Adversarial...
Diffusion models excel at generating visually striking content from text but can inadvertently produce undesirable or harmful content when trained on unfiltered internet data. A practical solution...