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Reactivity data for prediction models in chemical syntheses

PGR-P-701

Key facts

Type of research degree
PhD
Application deadline
Ongoing deadline
Project start date
Thursday 1 October 2020
Country eligibility
UK and EU
Funding
Funded
Source of funding
Research council
Supervisors
Dr Bao Nguyen
Schools
School of Chemistry
<h2 class="heading hide-accessible">Summary</h2>

Predictions models for reactivity have been one of the key underpinning tools for synthetic science. They are critically important in predicting reaction selectivity, synthetic route planning/reagent selection and in predicting impurities in the desired products. The development of these models has been difficult, despite recent advances in machine learning and data science, due to the lack of trustworthy kinetic and reactivity data in the open literature. The only rigorous source of reactivity data is Herbert Mayr&rsquo;s database, but its scope is limited.

<h2 class="heading hide-accessible">Full description</h2>

<p>Thus, there an urgent need for: (i) a robust sector-wide reactivity data collection framework, i.e. standardised reactions and measurements, in both chemical industry and academia; (ii) a mechanism to publish and share such data; and (iii) experimental methodologies to generate such data extremely quickly and efficiently without sacrificing data quality.</p> <p><span class="fontstyle0">We aim to address the issues highlighted above through an experimentally focused project (in conjunction with on-going machine learning activities in my group toward reactivity prediction). The objectives in this project are:</span></p> <p><strong><span class="fontstyle2">O1</span></strong><span class="fontstyle0">: Adjustments and application of the reactivity data protocol developed at Lhasa to nitrosamines,<br /> an important class of genotoxins.</span></p> <p><strong><span class="fontstyle2">O2</span></strong><span class="fontstyle0">: Development of competition reaction framework for reactivity/mechanistic data collection.</span></p> <p><strong><span class="fontstyle2">O3</span></strong><span class="fontstyle0">: Development of very high-throughput kinetic/reactivity data collection platforms based on<br /> non-contact spectroscopy and advanced data processing.</span></p> <p><span class="fontstyle0">You will obtain an interdisciplinary training, thanks to the unique nature of the project, which spans across synthetic chemistry, analytical technology, data science and highthroughput instrumentation, and my position in both the School of Chemistry and the School of Chemical and Process Engineering in Leeds. You&nbsp;will be encouraged to take ownership of the project and work out your own solution utilising help from experts. A seminar programme, where aspects of synthetic chemistry, process chemistry and chemical engineering are discussed on a monthly basis, will be provided. We expect you will have acquired a unique and highly transferable set of skills to join any process R&amp;D team when you graduate. Furthermore, opportunities to present work and to network will be provided through the <a href="https://www.iprd.leeds.ac.uk/">iPRD</a> industrial club meetings and the M62 network meetings. </span></p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://eps.leeds.ac.uk/chemistry-research-degrees/doc/apply">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is Reactivity Data for Prediction Models in Chemical Syntheses as well as <a href="https://eps.leeds.ac.uk/staff/4205/dr-bao-nguyen">Dr Bao Nguyen</a> as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<h2 class="heading">Funding on offer</h2>

<p>UK/EU &ndash; Engineering &amp; Physical Sciences Research Council CASE Studentships paying academic fees of &pound;4,600 for Session 2020/21, together with a maintenance grant paid at standard Research Council rates (&pound;15,285 for Session 2020/21) for 3.5 years. UK applicants will be eligible for a full award paying tuition fees and maintenance. European Union applicants will be eligible for an award paying tuition fees only, except in exceptional circumstances, or where residency has been established for more than 3 years prior to the start of the course. Funding is awarded on a competitive basis.</p>

<h2 class="heading">Contact details</h2>

<p>For further information regarding the project, please contact Doctoral College Admissions,<br /> e: <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a>, t: +44 (0)113 343 5057.</p> <p>For further information regarding the project, please contact Dr Bao Nguyen,<br /> e: <a href="mailto:b.nguyen@leeds.ac.uk">b.nguyen@leeds.ac.uk</a>, t: +44 (0)113 343 0109.</p>


<h3 class="heading heading--sm">Linked funding opportunities</h3>