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Paid Internship
Work Mode
Time Spent
Required Degree
Duration

8Open Positions

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PhD Student Researcher (Multimodal ML)

Interhuman AI
Found 2 months ago
Location
København, Denmark
Duration (Months)
1 Months
Time
Part-time
Work Mode
Not disclosed
Salary
8.000,00 kr./år - 10.000,00 kr./år
Visa Help
Not disclosed
Last Verified
1 month ago

Education

  • PhD

Skills & Qualifications

Technical Skills

  • Python
  • PyTorch
  • JAX
  • TensorFlow
  • LLM/Multimodal research landscape
  • CLIP
  • Audio-LMs
  • Video-Language models

Soft Skills

  • figuring things out independently
  • creative solutions
  • ask for help
  • explain technical concepts clearly in writing and speech
  • prioritizing and balancing multiple tasks
  • flexible when changes occur

Job Description

Interhuman AI is building the next generation of social intelligence infrastructure—multimodal AI systems that understand not just what humans say, but how they say it. We're developing models that interpret behavioral signals like hesitation, engagement, confusion, and interest across voice, facial expressions, body language, and natural language - in real time. We are looking for a Student Researcher to join our AI engineering team. This is not a typical "support" role; it is an invitation to apply the latest research in multimodal evaluation and data synthesis to a production-scale infrastructure. You will work on the practical foundations that allow our models to move from "experimental" to "state-of-the-art" every single week. What You * You are gonna own a scoped project that upgrades how we collect, validate, or evaluate complex behavioral-signal data, directly impacting model performance. * Create high-signal benchmarks to track improvements on the most challenging "long-tail" machine learning cases in social interaction. * Improve consistency and auditability across our pipeline so that our research results are trustworthy, repeatable, and scalable. * Contribute to the internal ecosystem of scripts and utilities that accelerate our experiment cycles, result comparison, and artifact organization.

Requirements

  • currently enrolled in a PhD program in Computer Science, Machine Learning, Data Science, or a related quantitative field (e.g., Physics, Mathematics, Statistics).
  • Strong proficiency in Python and experience with modern ML frameworks (PyTorch, JAX, or TensorFlow).
  • Deep familiarity with the current LLM/Multimodal research landscape (e.g., CLIP, Audio-LMs, Video-Language models).
  • comfortable figuring things out independently and don't need constant direction.
  • When you encounter obstacles, you find creative solutions and know when to ask for help.
  • can explain technical concepts clearly in writing and speech.
  • good at prioritizing and balancing multiple tasks, and flexible when changes occur.

Related Field

  • AI & Machine Learning

Related Subfield

  • Applied Machine Learning

Languages

  • English
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