SUNY Buffalo 招收Fall 2026 EE PhD
4160
𝗣𝗛𝗗 𝗣𝗢𝗦𝗜𝗧𝗜𝗢𝗡𝗦 (𝗙𝗔𝗟𝗟 𝟮𝟬𝟮𝟲) – 𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 × 𝗪𝗜𝗥𝗘𝗟𝗘𝗦𝗦 𝗡𝗘𝗧𝗪𝗢𝗥𝗞𝗦 @ linkedin.com-SUNY
Prof. Seyyedali Hosseinalipour is recruiting h͟i͟g͟h͟l͟y͟ ͟m͟o͟t͟i͟v͟a͟t͟e͟d PhD students (RA/TA fully-funded) with a strong background in mathematics to join my research group at the University at Buffalo (SUNY) for 𝗙𝗮𝗹𝗹 𝟮𝟬𝟮𝟲 (the hiring is rolling-based, so you can apply anytime).
Our work sits at the 𝗶𝗻𝘁𝗲𝗿𝘀𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗻𝗲𝘅𝘁-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝗿𝗲𝗹𝗲𝘀𝘀 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, with a strong emphasis on:
Federated Learning
LLMs and Foundation Models
Multi-modal and Distributed Learning
Wireless Edge Networks (UAVs, IoT, XR, etc.)
💡 𝗪𝗵𝗼 𝘀𝗵𝗼𝘂𝗹𝗱 𝗮𝗽𝗽𝗹𝘆?
We are looking for 𝗰𝘂𝗿𝗶𝗼𝘂𝘀, 𝗵𝗶𝗴𝗵-𝗲𝗻𝗲𝗿𝗴𝘆 individuals who:
Enjoy mathematical thinking and problem solving
Are excited about deep ML theory + systems integration
Thrive on challenging problems and learning new tools from scratch
Want to push the boundaries of distributed intelligence in real-world networks
This is a 𝗺𝗮𝘁𝗵-𝗱𝗿𝗶𝘃𝗲𝗻 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 ideal for candidates who genuinely enjoy theory, modeling, and rigorous thinking, while also caring about impactful applications.
📧 How to apply:
Please email your CV (and optionally transcripts/publications) to: 𝗮𝗹𝗶𝗽𝗼𝘂𝗿@𝗯𝘂𝗳𝗳𝗮𝗹𝗼.𝗲𝗱𝘂
📄 Recent representative work (please take a look at them before contacting me and provide some thoughts about them, which is not generated by ChatGPT 🙂):
Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection (IEEE/ACM ToN)
lnkd.in
Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities (IEEE TMI)
lnkd.in
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks (IEEE/ACM ToN)
lnkd.in
Prof. Seyyedali Hosseinalipour is recruiting h͟i͟g͟h͟l͟y͟ ͟m͟o͟t͟i͟v͟a͟t͟e͟d PhD students (RA/TA fully-funded) with a strong background in mathematics to join my research group at the University at Buffalo (SUNY) for 𝗙𝗮𝗹𝗹 𝟮𝟬𝟮𝟲 (the hiring is rolling-based, so you can apply anytime).
Our work sits at the 𝗶𝗻𝘁𝗲𝗿𝘀𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗻𝗲𝘅𝘁-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝗿𝗲𝗹𝗲𝘀𝘀 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, with a strong emphasis on:
Federated Learning
LLMs and Foundation Models
Multi-modal and Distributed Learning
Wireless Edge Networks (UAVs, IoT, XR, etc.)
💡 𝗪𝗵𝗼 𝘀𝗵𝗼𝘂𝗹𝗱 𝗮𝗽𝗽𝗹𝘆?
We are looking for 𝗰𝘂𝗿𝗶𝗼𝘂𝘀, 𝗵𝗶𝗴𝗵-𝗲𝗻𝗲𝗿𝗴𝘆 individuals who:
Enjoy mathematical thinking and problem solving
Are excited about deep ML theory + systems integration
Thrive on challenging problems and learning new tools from scratch
Want to push the boundaries of distributed intelligence in real-world networks
This is a 𝗺𝗮𝘁𝗵-𝗱𝗿𝗶𝘃𝗲𝗻 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 ideal for candidates who genuinely enjoy theory, modeling, and rigorous thinking, while also caring about impactful applications.
📧 How to apply:
Please email your CV (and optionally transcripts/publications) to: 𝗮𝗹𝗶𝗽𝗼𝘂𝗿@𝗯𝘂𝗳𝗳𝗮𝗹𝗼.𝗲𝗱𝘂
📄 Recent representative work (please take a look at them before contacting me and provide some thoughts about them, which is not generated by ChatGPT 🙂):
Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection (IEEE/ACM ToN)
lnkd.in
Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities (IEEE TMI)
lnkd.in
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks (IEEE/ACM ToN)
lnkd.in
