iMechanica - # machinelearning
https://imechanica.org/taxonomy/term/13433
enNew AI + Metamaterials postdoc position
https://imechanica.org/node/25930
<div class="field field-name-taxonomy-vocabulary-6 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/73">job</a></div></div></div><div class="field field-name-taxonomy-vocabulary-8 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/13431"># metamaterial</a></div><div class="field-item odd"><a href="/taxonomy/term/13434"># AI</a></div><div class="field-item even"><a href="/taxonomy/term/13433"># machinelearning</a></div><div class="field-item odd"><a href="/taxonomy/term/6340"># Finite Element modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p class="MsoNormal">We are seeking a creative and enthusiastic postdoc to work at Duke University as part of a collaborative DOE funded project entitled "<strong>FAIR Data and Interpretable AI Framework for Architectured Metamaterials</strong>". Candidate should have phd in mechanical or materials engineering, with experience in mechanical metamaterials, the underlying physical principles, finite element simulations, and preferably some knowledge of AI/ML and desire to learn more. Research will apply computation and AI/ML to develop FAIR datasets which will be deployed widely to the community and will develop new methods to understand the relationships between structure and dispersion curves in mechanical metamaterials. The methodologies will leverage interpretable machine learning strategies to discover structural patterns and to extend findings across length scales, opening the door for new scale bridging techniques. Ultimately these capabilities will be used to rapidly design new metamaterials matching a unique property space. A short statement of interest and CV should be sent to Cate Brinson at <a href="mailto:cbrinson.mdpostdoc@gmail.com">cbrinson.mdpostdoc@gmail.com</a> with copy to <a href="mailto:cate.brinson@duke.edu">cate.brinson@duke.edu</a>. Position available immediately for one year and may be renewed thereafter. Applications will be reviewed immediately and continue to be accepted until the position is filled.</p>
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<p class="MsoNormal">Recent paper from this work is here: <a href="https://arxiv.org/abs/2111.05949">https://arxiv.org/abs/2111.05949</a> </p>
<p class="MsoNormal">Public abstract of the project attached.</p>
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<tr class="odd"><td><span class="file"><img class="file-icon" alt="PDF icon" title="application/pdf" src="/modules/file/icons/application-pdf.png" /> <a href="https://imechanica.org/files/DOE%20FAIR%20public%20abstract%20v2_0.pdf" type="application/pdf; length=33922">DOE FAIR public abstract v2.pdf</a></span></td><td>33.13 KB</td> </tr>
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</div></div></div>Tue, 26 Apr 2022 23:55:49 +0000cbrinson25930 at https://imechanica.orghttps://imechanica.org/node/25930#commentshttps://imechanica.org/crss/node/25930Error | iMechanica