Internship Application

We are welcome to co-work with (to-be) researchers who take initiative and show strong willingness. To apply for our internship programs, you can choose either way: (1) pick one of our internship topics and email to the contact in the chosen topic announcement; (2) simply show your own interest to us (internmilab@gmail.com), no matter all of our topics below are closed. In your application email, we expect your transcript, expected period of internship, and the topics you are interested in. We prefer to pick an applicant who fits into our current projects or past publications, but it is not mandatory.

Experiencing our internship programs is NOT REQUIRED for joining our lab. To join our lab, you can directly email to Prof. Jung (kjung@snu.ac.kr).

Internship Contact

  • internmilab@gmail.com


Deep Learning for Natural Language Processing (Recruiting)

Description

Deep learning methods have been applied for solving a wide range of natural language processing (NLP) tasks such as dialog, summarization, and question answering. You can suggest any NLP tasks you want to attack. The recent publications of our lab would help to choose the task.

Candidate Qualifications

  • Strong attitude for the investigation
  • Basic python skills (required)
  • Understanding of recent algorithms in NLP literature. e.g., BERT.
  • Experience in implementing algorithms using PyTorch or Tensorflow

Expected Internship Period

  • Minimum three months - excluding period for the preliminary study (Pytorch, base ML, NLP basics)

Contact

  • internmilab@gmail.com

Neuro-symbolic Deep Learning for Logical Inference (Completed)

Description

Abilities to do logical inference, i.e. rule-based generalization, distinguish human intelligence from contemporary artificial intelligence (AI) implemented by deep learning models. To bridge this gap, studies about neuro-symbolic deep learning approach have been conducted. In this internship, we aim to digest existing works about the neuro-symbolic approach by reading papers and analyzing their implementation codes. To go further, we implement our own ideas and tackle challenging systematic generalization problems.

Candidate Qualifications

  • Strong and persistent attitude for the research (required)
  • Basic python skills (required)
  • PyTorch experience (optional)

Expected Internship Period

  • Minimum three months

Contact


End-to-end text-to-speech (Completed)

Description

Investigating end-to-end learning approaches to building a text-to-speech system that converts natural language text into human speech. The internship includes research, implementation, and experiment for target tasks above.

Candidate Qualifications

  • Strong attitude for the investigation
  • Basic python skills (required)
  • Understanding of recent algorithm in deep learning literature (i.e. Tacotron from google)
  • Experience in implementing an algorithm using PyTorch or Tensorflow

Expected Internship Period

  • Minimum four months - excluding period for the preliminary study (Pytorch, base ML algorithms)

Contact

  • Yoonhyung Lee (cpi1234@snu.ac.kr) (Completed)

Adversarial text generation (Completed)

Description

Adversarial Text generation aims at generating text with Generative adversarial networks (GANs) which can alleviate those drawbacks of MLE-based models such as exposure bias. You will learn the procedure of deep learning research which includes design and implementation of deep learning models. In addition, you will be involved in the research in progress.

Candidate Qualifications

  • Strong attitude for the investigation
  • Basic python skills (required)
  • Familiar with fundamental deep learning models (FNNs, CNNs, RNNs and etc)
  • Experience of implementing algorithms using deep learning open-source software library (Optional)

Expected Internship Period

  • Minimum three months - excluding period for the preliminary study (deep learning basics, library)

Contact

  • Yanghoon Kim (ad26kr@snu.ac.kr) (Completed)