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Efficient/Green-AI Researcher

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חובה
  • PhD or Master's degree in Computer Science, Electrical Engineering, or related fields
  • Experience in model optimization, hardware accelerators, and environmental impact assessment
  • Ability to design energy-efficient algorithms and hardware-aware model structures
  • Experience with model compression techniques such as quantization, pruning, and knowledge distillation
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    Home/Careers/Efficient/Green-AI Researcher

    Core AI Research

    Efficient/Green-AI Researcher

    This role focuses on reducing the environmental impact of AI and improving computational efficiency.

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    Overview

    This role focuses on reducing the environmental impact of AI and improving computational efficiency.

    Responsibilities

    • Optimize model architecture: Design energy-efficient algorithms and hardware-aware model structures.

    • Develop compression techniques: Work on quantization, pruning, and knowledge distillation to reduce resource consumption.

    • Evaluate carbon footprint: Measure energy usage of training and inference and propose ways to minimize it.

    • Collaborate with infrastructure teams: Work with HPC engineers and systems architects to implement efficient training pipelines.

    Qualifications

    • PhD or master's degree in computer science, electrical engineering, or related fields.

    • Experience in model optimization, hardware accelerators, and environmental impact assessment.

    Equal opportunity

    TAIRC evaluates candidates using role-related evidence and documented review criteria. Employment decisions must not be based on protected personal characteristics.

    Engagement pathways and compensation status

    TAIRC is currently building its funding base. Opportunities identified as volunteer or unpaid do not provide compensation at this stage. Any future paid appointment would require available funding, organizational approval, applicable legal compliance, and a separate written agreement. Participation does not guarantee future employment or compensation.

    • Volunteer contribution interest may be reviewed only as a voluntary, scoped pathway.

    • Educational internship interest requires separate supervision, educational purpose, and legal review.

    • Advisory expressions of interest are not board or formal advisory appointments.

    • Future compensated employment is contingent on funding, approval, legal compliance, and a separate written offer.

    Accessibility and accommodations

    For an accommodation or alternate submission format, contact hr@tairc.com. The application form does not request medical documentation or sensitive identity documents.

    Posting information

    Department Core AI Research

    Public role ID TAIRC-ROLE-011

    Status Open

    Location, employment type, workplace type, compensation, sponsorship policy, posted date, and closing date are omitted unless HR has entered approved factual values.

    Recruitment privacy

    Application data is used for recruitment review, stored with restricted access, and handled under the recruitment privacy and retention policy.

    אודות TAIRC
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    דומות וקשורות
    TAIRC
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