Team ARAM Membership Application
*PLEASE READ BEFORE APPLYING*

We offer high-quality hands-on supervision in the form of weekly project support and solid technical guidance in the domain of embodied intelligence and adjacent topics leveraging machine learning, optimization, and simulation techniques. Successfully completing our projects will require you to build intimate knowledge and skills for applying new and relevant methods in literature on high-impact problems.

As our capacity for supervision is limited, we select candidates based on their demonstration of elite-level curiosity and engagement. We will review your response and reach out for an interview if we consider you to be a good match for our culture and environment. If the interview is successful, you will be invited to come to one of our weekly meetings to give a 30-minute presentation on any topic of your choice, which will determine your final membership approval.

If you have any questions about the application process, please reach out to Nam Hee Gordon Kim: namhee.kim@aalto.fi

Thanks for your interest in joining Team ARAM!
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Correo electrónico *
Full name
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You are a good candidate for Team ARAM if you are all of the following:

* A naturally and genuinely curious person who constantly asks questions and makes observations
* An excellent team player who can give and receive constructive technical feedback
* Able to learn independently in the name of progress
* A good communicator who is proficient in English and capable of expressing complex scientific thoughts
* Willing to take accountability very seriously

Team ARAM is not for you if you are any of the following:

* Uncomfortable with leaning into unknowns and failures
* Looking for an easy or hands-off experience
* Shy, quiet, or uncomfortable with speaking up (note: this is different from introverted)
* Tolerant of being confused or afraid of looking stupid

In one paragraph, please explain why you are a good fit for Team ARAM.
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Any existing background in the following areas is a bonus, although not strictly required (we will ramp you up if you do not have these already):
* Understanding of basic concepts in machine learning (e.g., topics appearing in Nam Hee's UBC course: https://www.cs.ubc.ca/~nhgk/courses/cpsc340s21/)
* Experience with Python and its numerical computation and visualization libraries (e.g., numpy, torch, tensorflow, keras, matplotlib, etc., and/or coursework involving exercises equivalent to that of Stanford CS231n: http://cs231n.stanford.edu/)
* Understanding of basic concepts in deep reinforcement learning (e.g., topics appearing in UC Berkeley CS235: https://rail.eecs.berkeley.edu/deeprlcourse/)
* Experience with physics simulators: rigid-body, fluid, or otherwise (e.g., Bullet, MuJoCo, Taichi, etc.)

If you have any of the above background skills and experiences, please elaborate your experience (e.g., course name and institution, project involving Python and the libraries mentioned, etc.)
Personal webpage
Why are you interested in animation, robotics, and machine learning research?
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Research interests

Please write a few sentences about what specific topics you'd like to work on if you were to join us.
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URL of your resume / CV
URL of the code sample you are most proud of (if available)
URL of the project you are most proud of (can be a paper; source code not required)
Current Year of Studies
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Do you have previous work experience in industry, including either software engineering or research or both?
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Have you contributed to any research publications?
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What is more important for you to accomplish during your time at Team ARAM?
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