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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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<p><a href="group/itamblyn.html">Isaac Tamblyn</a>, (Ph.D. Dalhousie)<br>he/him </p>
<p>Adjunct Professor, <a href="http://science.uottawa.ca/physics/">University of Ottawa (Physics)</a>, <a href="https://uwaterloo.ca/electrical-computer-engineering/">University of Waterloo (Electrical & Computer Engineering)</a>, and <a href="https://science.ontariotechu.ca/physics/index.php">Ontario Tech University (Physics)</a><p>
<p>e-mail : <a href="mailto:isaac.tamblyn@uottawa.ca">isaac.tamblyn@uottawa.ca</a> (<a href="https://keys.openpgp.org/vks/v1/by-fingerprint/2709BDE74034DB6A4842E1D03259FEF2EA5C4C99">public key<a>)<br>
<p>tel : +1.343.202.4021</p>
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<p>
<h3>Machine Learning at the Nanoscale for Renewable Energy</h3>
<p> The main objective of my research is to apply computational methods to applied problems in material science. Recently, our group has been focused on applying the tools of "data science" to the problem of materials. We are interested particularly interested in using recent advances in deep learning learning and A.I. to develop computationally tractable solutions to hard problems in condensed matter physics, with a focus on applications in the space of renewable energy materials. </p>
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<p>Electronic excitations tuned to the solar spectra can create electron-hole pairs suitable for <u><a href="research.html">artificial photosynthesis and water splitting</a></u>.</p>
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