Research Intern

5 hours ago

Boston, Massachusetts, United States Daice Labs Full time

Company Description

Daice Labs, founded by MIT CSAIL scientists, specializes in developing innovative hybrid AI systems that integrate large language models (LLMs), deep learning (DL), symbolic reasoning, and bio-inspired designs. With the goal of overcoming the limitations of current AI systems, Daice Labs builds adaptive frameworks capable of continuous learning, generalization, and decision-making. The organization operates through two primary focus areas: a Product Lab, creating platforms for human teams-AI collaboration on complex projects, and an AI Research Lab, exploring natural intelligence principles to design effective and adaptable AI.

Role Description

This is a full-time remote position for a Research Intern at Daice Labs. The Research Intern will contribute to the investigation and development of novel hybrid AI architectures inspired by principles of natural intelligence. Responsibilities include assisting in research on topics such as bio-inspired system design, perturbation-aware learning, rule-guided frameworks, and AI generalization. The candidate will support the synthesis of new models, analyze complex datasets, and contribute to publications and presentations on research findings.

Qualifications

  • Strong foundations in Machine Learning, AI, and computational theories
  • Familiarity with symbolic reasoning, reinforcement learning, or deep learning frameworks
  • Analytical skills with strong aptitude in data analysis and algorithm development
  • Proficiency in programming languages such as Python or relevant technical tools
  • Interest in bio-inspired design, natural intelligence, or system-level AI research
  • Excellent problem-solving abilities and the capacity for independent and collaborative work
  • Current enrollment in or completion of a degree program in a relevant scientific or technical field (e.g., Computer Science, AI, Mathematics, or similar)
  • Strong communication skills to effectively present technical concepts and findings

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