Machine Learning

We are on the lookout for passionate and skilled interns to join our cutting-edge research team focused on developing efficient machine learning techniques for sustainability. This is an exciting opportunity to make a real impact in the field of AI and environmental conservation, with the aim of publishing at leading AI research venues.

What We're Looking For:

• Advanced research experience in generative models and computationally efficient models
• Hands-on skills for large language models (LLM), vision language models (VLM), large multi-modal models (LMM), foundation models (FoMo)
• Deep understanding of state-of-the-art machine learning methods
• Proficiency in Python and PyTorch
• Familiarity with various deep learning frameworks
• Ph.D. candidates who have completed at least half of their program

Internship Details:

• Duration: approximately 3 months
• Flexible start dates available
• Objective: publish research results at leading AI research venues

If you are a highly motivated individual with a passion for applying AI to sustainability challenges, we want to hear from you! This internship offers a unique chance to work on meaningful projects at the intersection of machine learning and environmental sustainability.

MERL is looking for a self-motivated intern to work on anomaly localization in industrial inspection setting using computer vision. The relevant topics in the scope include (but not limited to): cross-view image anomaly localization, how to train one model for multiple views and defect types, how to incorporate large foundation models in image anomaly localization, etc. The candidates with experiences of image anomaly localization in industrial inspection settings (e.g., MVTec-AD or VisA datasets) and usage of large foundation models are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.

Required Specific Experience

• Experience with Python, PyTorch, and large foundation models (e.g. CLIP, ALIGN, etc.).

MERL is seeking a highly motivated and qualified intern to carry out research on mobile IoT network design methodology. The candidate is expected to develop innovative mobile network technologies to support UAV assisted IoT networks. The candidates should have knowledge of mobile network technologies such as path planning and cooperative network operations. Knowledge of UAV technology and mobility management is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

The responsibilities of this intern position include (i) research on UAV assisted network design methodology; (ii) develop network configuration technologies to support UAV cooperative network operations; (iii) simulate and analyze the performance of developed technology.