LLVM/MLIR Meet-up Recap

ML2 @ AsiaLLVM 2025 

ML2 졸업생 인터뷰 - 🎒 KAIST EE Co-op편2

ML2 졸업생 인터뷰 -  ✈️ 유학편2

Blockchain Graph Analysis using GNN 

Image Retrieval based Robot Global Localization

euroLLVM2023

Spot the Difference with LLVM-FLOW: an open-source interactive visualization tool for comparing IR CFGs

ML2 졸업생 인터뷰 - 🎒 KAIST EE Co-op편

Symbolic Simplifier with Deep Learning

Advanced Decision Tree Techniques for Various Applications

Efficiently Learning the Value Distribution for Actor-Critic Methods

오픈소스를 꿈꾸는 혁신가를 위한 안내서 🚀

ML2 졸업생 인터뷰 - ✈️ 유학편

DIYA의 2020년 머신러닝 프로젝트 소개

GraphDB and Graph Neural Network

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

오픈소스 라이선스 가이드

Value Function Geometry

CodeFest2020 결과 발표회

🎉 CodeFest2020 🎉 을 개최합니다

ML2 multiagent RL mini-game environments

LLVM

DIYA의 2019년 머신러닝 프로젝트 소개

Investigating Pixel Robustness using Input Gradients

Interesting research topics from ICML 2018

Distilling Curiosity for Exploration

DARTS (Differentiable ARchiTecture Search)

Deep Joint Unrolling for Deblurring and Low-Light Image Enhancement (JUDE)

Enhancing OCR-based Indoor Place Recognition with Visitor Map Image by Mitigating Noise from Distracting Words

Sample-efficient inverse design of freeform nanophotonic devices with physics-informed reinforcement learning

In-Season Wall-to-Wall Crop-Type Mapping Using Ensemble of Image Segmentation Models

RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning

Free-form optimization of nanophotonic devices: from classical methods to deep learning

Structural optimization of a one-dimensional freeform metagrating deflector via deep reinforcement learning

Inverse design of organic light-emitting diode structure based on deep neural networks

GMAC: A Distributional Perspective on Actor-Critic Framework

<h5 id="what-youll-do">What you'll do</h5>
<ul>
<li>Following recent developments in machine learning and sharing them with ML2 members and others via seminars and/or blog posts.</li>
<li>Re-implementing cutting-edge machine learning models and reproducing experimental results.</li>
<li>Software development based on open-source machine learning frameworks for research and development in machine learning and deep learning.</li>
</ul>
<h5 id="details">Details</h5>
<ul>
<li>KRW 35M (negotiable)</li>
<li>Duration
<ul>
<li>3-month</li>
<li>After the internship finishes, participants with exceptional performance are eligible for a full-time position via an internal interview.</li>
</ul>
</li>
</ul>
<hr>
<h5 id="job-description">Job Description</h5>
<p><strong>About the Role</strong></p>
<ul>
<li>You have a great potential but lack experiences in the field? Then the internship position is right here for you!</li>
</ul>
<p><strong>Minimum qualification</strong></p>
<ul>
<li>Experience in academic research.</li>
<li>Experience in implementing machine learning models.</li>
<li>Ability to understand machine learning research papers and implement models described in the literatures.</li>
</ul>
<p><strong>Preferred qualification</strong></p>
<ul>
<li>Track record of self-motivated, independent, and collaborative academic research and/or software development.</li>
<li>Publications in academic journals and conferences in machine learning including NIPS, ICML, and ICLR.</li>
<li>Experience in large-codebase open-source projects of developing machine learning models using open-source machine learning frameworks such as TensorFlow or PyTorch.</li>
</ul>
<hr>
<h5 id="hiring-process">Hiring process</h5>
<ol>
<li>Check the <a href="https://www.kc-ml2.com/en/careers_info">guide</a>.</li>
<li>Send mail to : <a href="mailto:contact@kc-ml2.com">contact@kc-ml2.com</a></li>
</ol>

---
title: 'Internship (English)'
date: '마감'
state: '마감'
cate: 'career'
type: '계약직'
---

##### What you'll do

- Following recent developments in machine learning and sharing them with ML2 members and others via seminars and/or blog posts.
- Re-implementing cutting-edge machine learning models and reproducing experimental results.
- Software development based on open-source machine learning frameworks for research and development in machine learning and deep learning.

##### Details

- KRW 35M (negotiable)
- Duration
  - 3-month
  - After the internship finishes, participants with exceptional performance are eligible for a full-time position via an internal interview.

---

##### Job Description

**About the Role**

- You have a great potential but lack experiences in the field? Then the internship position is right here for you!

**Minimum qualification**

- Experience in academic research.
- Experience in implementing machine learning models.
- Ability to understand machine learning research papers and implement models described in the literatures.

**Preferred qualification**

- Track record of self-motivated, independent, and collaborative academic research and/or software development.
- Publications in academic journals and conferences in machine learning including NIPS, ICML, and ICLR.
- Experience in large-codebase open-source projects of developing machine learning models using open-source machine learning frameworks such as TensorFlow or PyTorch.

---

##### Hiring process

1. Check the [guide](https://www.kc-ml2.com/en/careers_info).
2. Send mail to : contact@kc-ml2.com


Internship (English)