[CS/ML]Funded PhD positions at the University of Arizona
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大家好，我为我的导师Kwang-Sung Jun转发一下PhD招生信息，主要关注的是偏理论一点的ML。Kwang是非常非常nice以及supportive的导师，大多数成果都是在COLT，ICML，NeurIPS上。
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[Recruiting machine learning PhD students (funded position) from the Department of Computer Science at the University of Arizona]
My name is Kwang-Sung Jun (kwangsungjun.github.io), an assistant professor who is interested in interactive machine learning and adaptive decision-making/sampling methods including bandit algorithms and monte carlo tree search methods. Applications include efficient matrix decomposition, geoscience (some blackbox/bayesian optimization involved), material science problems, and more. Required background is probability, statistics, linear algebra, calculus, and some CS background. Background in ML theory is a plus. Your undergrad major does not matter, but minimal CS background is required (we can discuss it further). For this position, you should expect that you will spend a lot of time on mathematical research (e.g., algorithms with performance guarantees) as opposed to tuning hyperparameters on benchmark datasets. This, however, does not mean we will only stick to theories. We will develop performant algorithms and test them in real-world tasks. You can find my email from my webpage and send me an email if you are interested.
Information about our PhD program and the life here:
You do not need a master's degree.
PhD students will receive an RAship or otherwise have a guaranteed TA funding up to 4 years, which means you will receive a stipend and need not pay the tuition.
We are located in Tucson, Arizona, which has about 1M population in the metropolitan area. Quality of life can be better than usual college cities.
Living cost is lower compared to the size of the city.
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[Recruiting machine learning PhD students (funded position) from the Department of Computer Science at the University of Arizona]
My name is Kwang-Sung Jun (kwangsungjun.github.io), an assistant professor who is interested in interactive machine learning and adaptive decision-making/sampling methods including bandit algorithms and monte carlo tree search methods. Applications include efficient matrix decomposition, geoscience (some blackbox/bayesian optimization involved), material science problems, and more. Required background is probability, statistics, linear algebra, calculus, and some CS background. Background in ML theory is a plus. Your undergrad major does not matter, but minimal CS background is required (we can discuss it further). For this position, you should expect that you will spend a lot of time on mathematical research (e.g., algorithms with performance guarantees) as opposed to tuning hyperparameters on benchmark datasets. This, however, does not mean we will only stick to theories. We will develop performant algorithms and test them in real-world tasks. You can find my email from my webpage and send me an email if you are interested.
Information about our PhD program and the life here:
You do not need a master's degree.
PhD students will receive an RAship or otherwise have a guaranteed TA funding up to 4 years, which means you will receive a stipend and need not pay the tuition.
We are located in Tucson, Arizona, which has about 1M population in the metropolitan area. Quality of life can be better than usual college cities.
Living cost is lower compared to the size of the city.
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