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<span style="margin:0px;font-size:12pt;color:rgb(32, 31, 30) !important;background-color:rgb(255, 255, 255) !important">Applications are invited for a 4 year fully funded PhD project within the Operational Research Group, School of Mathematical Sciences, University
of Southampton</span><span style="margin:0px;font-size:15px;color:rgb(32, 31, 30) !important;background-color:rgb(255, 255, 255) !important;display:inline !important"></span><span style="margin:0px;background-color:rgb(255, 255, 255) !important;display:inline !important"></span><span style="background-color:rgb(255, 255, 255);display:inline !important"></span>
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<b>Project title:<span style="margin:0px"> </span></b>Optimising machine learning algorithms for industrial applications<br>
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<div style="margin:0px"><b>Project description:</b><span style="margin:0px"> </span>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.</div>
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<div style="margin:0px">More details available here: <a href="https://jobs.soton.ac.uk/Vacancy.aspx?ref=1616821PJ" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="0" style="margin:0px">https://jobs.soton.ac.uk/Vacancy.aspx?ref=1616821PJ</a></div>
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<div style="margin:0px"><b>Funding:</b><span style="margin:0px"> </span>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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<div style="margin:0px">Dr Alain Zemkoho</div>
<div style="margin:0px">Associate Professor</div>
<div style="margin:0px">School of Mathematical Sciences</div>
University of Southampton</span></div>
<div style="margin:0px"><span style="margin:0px;font-size:12pt">Email: a.b.zemkoho@soton.ac.uk<br>
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<span style="margin:0px"><span style="margin:0px;font-size:12pt"><a href="https://www.southampton.ac.uk/~abz1e14/" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="1" style="margin:0px">https://www.southampton.ac.uk/~abz1e14/</a></span></span></div>
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