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<p><br>
Dear Neuromorphs,<br>
<br>
The following position is open in my team at TU Delft:<br>
<u><br>
PhD/Postdoc position </u><u>[Integrated circuit design, hardware/algorithm co-design]
</u><u>- Brain-inspired hardware for edge continual learning </u><br>
</p>
<blockquote>
<p><i>Biological systems can learn and adapt robustly over a sequence of new experiences. This comes in stark contrast with today's edge computing devices, which are unable to autonomously adapt to their environment. This standard train-then-deploy strategy
assumes </i><i>a static and controlled environment, which</i><i> is not representative of the real world where key characteristics of the user/environment can change over time.<br>
You will tackle this challenge by endowing smart devices with low-power long-term autonomous adaptation. To do so, you will<br>
- merge the latest neuroscience and machine-learning research in continual learning,<br>
- follow a co-design approach to translate your findings into custom silicon,<br>
- tape out your own digital integrated circuit(s) to demonstrate sub-mW continual-learning edge hardware (mixed-signal is also an option).</i></p>
</blockquote>
<p>Link to the vacancy and application guidelines: <a class="moz-txt-link-freetext" href="https://microelectronics.tudelft.nl/Openings/vacancy.php?id=187">
https://microelectronics.tudelft.nl/Openings/vacancy.php?id=187</a><br>
<br>
The position is open until filled - early application is encouraged!<br>
<br>
Kind regards,<br>
Charlotte<br>
</p>
<pre class="moz-signature" cols="72">Charlotte Frenkel
Assistant professor, TU Delft
EEMCS faculty - Microelectronics department
Mekelweg 4 (15.290), 2628 CD Delft, The Netherlands
Website: <a class="moz-txt-link-freetext" href="https://chfrenkel.github.io">https://chfrenkel.github.io</a></pre>
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