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<a class="nova-e-link nova-e-link--color-inherit nova-e-link--theme-bare" href="https://www.researchgate.net/project/project/Metabolic-network-analytics/update/5e16d164cfe4a777d4025d0d?_iepl%5BviewId%5D=ElYujPo1gU3ZVjIR9u5d6xB1&_iepl%5Bcontexts%5D%5B0%5D=projectUpdatesLog&_iepl%5BinteractionType%5D=projectUpdateDetailClickThrough" style="color: inherit; text-decoration: none; cursor: pointer; outline: none; padding: 0px; margin: 0px; font-size: 22px; border: 0px; background-image: none; display: inline;">PhD
position in AI for synthetic biology open</a></div>
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KEPACO research group (<a href="http://research.cs.aalto.fi/kepaco/" class="">research.cs.aalto.fi/kepaco/</a>) in Aalto university (<a href="http://www.aalto.fi/" class="">www.aalto.fi</a>) is looking or a PhD student in the field of Artificial intelligence
for Synthetic Biology.</div>
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Synthetic biology has a large potential to tackle the issues of circular economy, from converting waste to useful products to carbon-neutral industrial production, through the use of synthetically engineered microbes. These engineering efforts can potentially
be expedited with AI-assisted design processes, however, this potential still largely unharnessed. We envision a AI system facilitating the DBTL cycle (Design, Build, Test, Learn), where the strain is designed (D), built in a laboratory (B), measured and tested
(T), to learn (L) a model on the current strain to be exploited for the next cycle design phase (D) again. Automatic operation of the DBTL loop has so far been been demonstrated only in selected settings.</div>
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In this project, we propose to develop new AI approaches to tackle the problem of accelerating the design of synthetic microbial strains. In short, we propose learning a model to suggest modifications to an existing design by a reinforcement learning approach,
where the feedback from the testing of a design is used to propose new, improved designs. Such a model has been recently shown to be promising approach for finding genetic modifications that maximise the productivity of synthetic microbial strain. The project
will be conducted in collaboration with Synthetic biology team at VTT Technical Research Center of Finland.</div>
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The ideal background for the student is an MSc in computer science, mathematics or statistics, with strong experience in machine learning. Experience in reinforcement learning, robotics, control systems engineering, synthetic biology or systems biology is considered
as an advantage. </div>
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To apply, fill in an application form at <a class="nova-e-link nova-e-link--color-inherit nova-e-link--theme-decorated" href="https://www.researchgate.net/deref/https%3A%2F%2Fwww.hict.fi%2Fspring2020" target="_blank" rel="noopener" style="color: inherit; cursor: pointer; outline: none; padding: 0px; margin: 0px; font-size: inherit; font-family: inherit; border: 0px; background-image: none; text-align: inherit; display: inline;">https://www.hict.fi/spring2020</a> by
the call deadline Mon, 27.01.2020</div>
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Questions about the position: <a href="mailto:juho.rousu@aalto.fi" class="">email juho.rousu@aalto.fi</a></div>
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