AI Research Supervisor

About HyperQuark Intelligence Labs HyperQuark Intelligence Labs™ is an applied artificial intelligence research initiative focused on exploring next-generation intelligence systems that combine large language models, structured knowledge systems, and reasoning‑centric architectures.

Our research investigates how AI systems can move beyond purely generative pattern matching toward structured intelligence, integrating:

Knowledge graphs and structured representations

Reasoning‑centric AI architectures

Skill inference and behavioral learning models

AI systems capable of modeling human capability, experience, and decision pathways

One of our core initiatives involves the development of the Dynamic Career Intelligence Graph, an evolving knowledge system designed to represent skills, experience, and human potential in a structured, machine‑interpretable form. This work is closely connected with the broader ZENOR‑Æ intelligence platform, which aims to build AI systems that support human growth, career intelligence, and decision‑making.

HyperQuark operates as an independent research collaboration environment bringing together researchers, engineers, and interdisciplinary thinkers who are interested in experimenting with new AI architectures and exploring emerging research questions at the intersection of machine learning, knowledge systems, and human intelligence modeling.

Role Description We are inviting experienced researchers, PhD candidates, and applied AI practitioners to join HyperQuark Intelligence Labs as Research Supervisors / Research Collaborators for our upcoming research cohort.

This role focuses on guiding experimental research tracks and supporting fellows who are exploring applied AI systems in areas such as knowledge representation, reasoning models, and structured intelligence architectures.

Participants in this role will collaborate with a cohort of researchers and fellows working on exploratory projects involving:

Knowledge graphs and structured representation systems

LLM reasoning architectures

AI systems for skill inference and decision intelligence

Evaluation frameworks for AI explainability and fairness

Experimental prototypes and research artifacts

The role is structured as a remote, part‑time research collaboration (~6–8 hours per week) over a 12‑week research cycle.

Research supervisors and collaborators help shape discussions, guide technical exploration, and contribute to the research direction of the cohort while participating in an open and intellectually collaborative research environment.

This role is particularly well‑suited for individuals who enjoy mentoring early researchers, discussing emerging research ideas, and contributing to experimental AI research outside traditional institutional settings.

Key Responsibilities Research Guidance

Support fellows in refining research questions and experimental approaches.

Help guide exploration of literature and research methodologies.

Provide feedback on experimental design and technical direction.

Research Discussions

Participate in weekly research discussions and reading sessions.

Facilitate conversations around emerging AI research topics.

Encourage critical thinking and interdisciplinary exploration.

Prototype Development

Contribute to experimental AI systems and research prototypes.

Support development of proof‑of‑concept systems exploring new architectures.

Help evaluate approaches for reasoning systems and knowledge integration.

Research Output

Collaborate on technical reports and experimental research notes.

Contribute to open research artifacts and prototype demonstrations.

Support development of potential research publications.

Qualifications We are particularly interested in candidates who have strong research curiosity and experience exploring advanced AI systems.

Academic Background

PhD candidate, PhD holder, or advanced researcher in

Computer Science

Artificial Intelligence

Machine Learning

Data Science

Computational Linguistics

Knowledge Representation

Related technical disciplines.

Technical Experience

Machine learning and deep learning systems.

Large language model experimentation and architectures.

Reinforcement learning and optimization methods.

Knowledge graphs and structured representation systems.

AI reasoning systems and agent architectures.

NLP systems and semantic modeling.

Model evaluation and interpretability frameworks.

Research Experience

Experience working on research projects, publications, or open‑source AI systems.

Familiarity with experimental research workflows and model evaluation.

Ability to guide discussions around emerging research ideas.

Technical Tools

Python

PyTorch or TensorFlow

Hugging Face ecosystem.

Graph databases or knowledge graph frameworks.

Data analysis and experimental modeling tools.

Additional Qualities

Enjoy mentoring and supporting early researchers.

Curious about emerging AI architectures and experimental systems.

Interested in open research environments and collaborative exploration.

Strong interest in AI systems that combine structured knowledge and reasoning.

Program Structure

Duration: 12 weeks.

Commitment: Approximately 6–8 hours per week.

Format: Fully remote collaboration.

Activities: Research discussions, literature exploration, experimental prototyping, technical documentation and reports.

Participants collaborate in small research groups and contribute to experimental research initiatives aligned with HyperQuark’s broader research direction.

Compensation At this stage, the HyperQuark Research Fellowship is an independent research collaboration initiative, and the program is currently unpaid.

The focus of the program is on collaborative research exploration, hands‑on experimentation with AI systems, contribution to research prototypes and technical outputs, and building a community of researchers exploring emerging intelligence systems.

Who This Role Is Ideal For

PhD students or researchers interested in mentoring early AI researchers.

Practitioners exploring research outside traditional institutional labs.

Individuals interested in contributing to experimental AI research initiatives.

Researchers who enjoy collaborative thinking and building new research directions.

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