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            <h1 id="join-title">Build open vision intelligence with a global research community.</h1>
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              OpenEnvision welcomes students, researchers, engineers, and institutional partners who
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            <span>World models, multimodal intelligence, vision intelligence, and physical intelligence.</span>
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          <div><span>01</span><strong>Joint research</strong></div>
          <div><span>02</span><strong>Open-source systems</strong></div>
          <div><span>03</span><strong>Datasets and benchmarks</strong></div>
          <div><span>04</span><strong>Academic-industry partnership</strong></div>
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            <h2 id="join-opportunities-title">Different ways to join the work, held to one research standard.</h2>
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              <p>Students and Visitors</p>
              <h3>Research Students, Visiting Researchers, and Interns</h3>
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              <p>Research Collaboration</p>
              <h3>Faculty, Scientists, and Research Teams</h3>
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              <p>Open Infrastructure</p>
              <h3>Open-Source Engineers and Dataset Builders</h3>
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              Contributors can help with training and evaluation code, demos, documentation,
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              <p>Institutional Partners</p>
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              We look for partnerships with complementary expertise, credible data or deployment
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            <h2 id="join-fit-title">The best fit is precise, open, and technically grounded.</h2>
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              OpenEnvision is built around public research artifacts. We value work that is
              ambitious enough to move the field and careful enough for others to inspect, reuse,
              reproduce, and extend.
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          <div class="join-value-list" aria-label="OpenEnvision collaboration values">
            <article data-join-reveal><span>01</span><strong>Research Taste</strong><p>Clear problem framing, thoughtful baselines, and an instinct for questions that matter.</p></article>
            <article data-join-reveal><span>02</span><strong>Execution</strong><p>Reliable code, clean experiments, strong writing, and visible progress under ambiguity.</p></article>
            <article data-join-reveal><span>03</span><strong>Evaluation Discipline</strong><p>Metrics, protocols, ablations, and qualitative analysis that support real claims.</p></article>
            <article data-join-reveal><span>04</span><strong>Open Release Mindset</strong><p>Reusable artifacts, documentation, licenses, model or data cards, and versioned updates.</p></article>
            <article data-join-reveal><span>05</span><strong>Collaboration Maturity</strong><p>Respectful communication, clear ownership, and readiness to work across institutions.</p></article>
            <article data-join-reveal><span>06</span><strong>Responsible Practice</strong><p>Attention to data provenance, limitations, intended use, and community impact.</p></article>
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