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<p class="MsoNormal"><span style="font-size:12.0pt;mso-fareast-language:EN-US">***apologies if you receive this more than once***<o:p></o:p></span></p>
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<p class="MsoNormal"><u><span style="font-size:12.0pt;mso-fareast-language:EN-US">Circulated on behalf of Dr Alain Zemkoho<o:p></o:p></span></u></p>
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<p class="MsoNormal"><span style="font-size:12.0pt">Applications are invited for a 4 year fully funded <span class="mark1om7r0rnj">PhD</span> project within the Operational Research Group, School of Mathematical Sciences, University of Southampton</span>
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<p class="MsoNormal"><b><span style="font-size:11.5pt">Project title: </span></b><span style="font-size:11.5pt">Optimising machine learning algorithms for industrial applications<o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:11.5pt">Project description:</span></b><span style="font-size:11.5pt"> The aim of this project is to take a very practical approach to machine learning, designing learning algorithms tailored to practical applications
using real world data. The plan is to consider various tasks, mostly focused on supervised learning, including classification and regression tasks, developing support vector machines and decision trees-based algorithms, as well as taking advantage of the infrastructure
of deep learning methods where necessary. Each of these techniques involves the calculation of one or several hyperparameters, which are crucial for their performance. Hence, the development of the machine learning algorithms expected in this project will
take a broad approach, going from the basic training step to the design of powerful hyperparameter algorithms, possibly taking advantage of the hierarchical nature of the hyperparameter optimization problem. The methods to be developed will be driven by applications,
as the industrial funding of the project is provided by Decision Analysis Services Ltd (DAS), which has a wide range of clients, from management to highly technical engineering companies. Therefore, algorithms are expected to be tested on a varied base of
data sets, from small to very large-scale time series or cross-sectional datatypes.<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.5pt">More details available here: <a href="https://jobs.soton.ac.uk/Vacancy.aspx?ref=1616821PJ" target="_blank">https://jobs.soton.ac.uk/Vacancy.aspx?ref=1616821PJ</a><o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:11.5pt">Funding:</span></b><span style="font-size:11.5pt"> The project is fully funded, jointly by DAS and the School of Mathematical Sciences, University of Southampton, and covers full tuition fees at UK rates,
and a stipend of £15,285 tax-free per annum for up to 4 years.<br>
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<p class="MsoNormal"><span style="font-size:12.0pt">--------</span><span style="font-size:11.5pt"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:12.0pt">Dr Alain Zemkoho<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:12.0pt">Associate Professor<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:12.0pt">School of Mathematical Sciences<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:12.0pt">University of Southampton</span><span style="font-size:11.5pt"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:12.0pt">Email: <a href="mailto:a.b.zemkoho@soton.ac.uk">
a.b.zemkoho@soton.ac.uk</a></span><span style="font-size:11.5pt"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:12.0pt"><a href="https://www.southampton.ac.uk/~abz1e14/" target="_blank">https://www.southampton.ac.uk/~abz1e14/</a></span><span style="font-size:11.5pt"><o:p></o:p></span></p>
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