http://jobs.aston.ac.uk/Vacancies.aspx?cat=542&type=6 Latest job vacancies at Aston http://jobs.aston.ac.uk/rss/click.aspx?ref=R130171 http://jobs.aston.ac.uk/rss/click.aspx?ref=R130171 Category: Teaching & Research | Group: Engineering & Applied Science | Closing Date: 08 Jul 2013 | Working within the Polymers and Advanced Materials Research Group, you will carry out research in the Polymer Synthesis research facilities at Aston University.  This will involve the collaboration with project partner, Robinson Brothers Ltd, with a range of technical responsibilities. You will be assigned to a project synthesising well-defined polymers via RAFT Polymerisation and probing the kinetics of the cross-linking process of the resultant polymers.  You will be responsible for establishing an appropriate controlled synthetic route (via RAFT) and to establish the means to monitor the curing process under various cure conditions. You should have a First degree in Chemistry or related subject (2:1 or higher), and should be local to the company , Robinson Brothers Ltd, based in the West Midlands.  Training opportunities will be provided to acquire appropriate experimental and analytical research skills. For further information about this position, please contact Dr Paul D Topham, p.d.topham@aston.ac.uk. Teaching & Research Fri, 07 Jun 2013 00:00:00 GMT http://jobs.aston.ac.uk/rss/click.aspx?ref=R130150 http://jobs.aston.ac.uk/rss/click.aspx?ref=R130150 Category: Studentship | Group: Engineering & Applied Science | Closing Date: 30 Jun 2013 | Applications are invited for a three-year PhD studentship, supported by the School of Engineering and Applied Science, to be undertaken within the Nonlinearity and Complexity Research Group (http://www1.aston.ac.uk/eas/research/groups/ncrg/) at Aston University.  The successful applicant will join an established experimental group working on machine learning and social media analysis. The position is available to start in October 2013 (subject to negotiation) Financial Support This studentship includes a fee bursary to cover the home/EU fees rate plus a maintenance allowance of £13,590 (increases on an annual basis). Applicants from outside the EU may apply for this studentship but will need to pay the difference between the ‘Home/EU’ and the ‘Overseas’ tuition fees.  This will be £11,010 in the academic year 2013/14. Background of the Project In the online social media space, it is often observed that users connected with each other are likely to express similar opinions. Such connections can be established by either explicit connections, e.g., a user follows another in Twitter or one is another’s friend in Facebook, or social interactions, e.g., a user posts another one’s message to his/her followers in Twitter (called retweet) or users express their support on someone’s post by clicking on the “Like” button in Facebook. Such social interactions are often viewed as endorsements of one’s support of another’s opinion. In many models of opinion formation, two basic social psychological mechanisms can be identified: social influence and homophily. While opinion diffusion patterns might be created by social influence, they might also be created because of dyadic similarities among neighbouring nodes in social networks without causal influence (homophily), or external influences such as marketing or mass advertising (confounding factors). Project This PhD project aims to investigate what factors influence opinion formation and how opinions diffuse over implicit social networks on the social web using techniques from statistical modelling, machine learning, natural language processing, and data mining. In particular, it will develop novel models for opinion diffusion analysis so as to facilitate opinion prediction. Person Specification The successful applicant should have a first class or upper second class honours degree or equivalent qualification in Computer Science, Mathematics, Statistics or a related discipline. Knowledge of machine learning or natural language processing would be desirable.   For informal enquiries about this opportunity, contact Dr Yulan He (email: y.he9@aston.ac.uk, tel.: +44 (0)121 204 5329). The online application form, reference forms and details of entry requirements, including English language are available at http://www1.aston.ac.uk/eas/research/prospective-research-students/how-to-apply/ Closing Date: 30th June 2013 Studentship Fri, 24 May 2013 00:00:00 GMT http://jobs.aston.ac.uk/rss/click.aspx?ref=R130091 http://jobs.aston.ac.uk/rss/click.aspx?ref=R130091 Category: Studentship | Group: Engineering & Applied Science | Closing Date: 01 Jul 2013 | Machine Learning in Signal Processing The Nonlinearity and Complexity Research Groupat Aston University are pleased to invite applications for a three year PhD studentship in machine learning applied to signal processing, supervised by Dr Max Little, supported by the School of Engineering and Applied Science. The successful applicant will join an established group and will work on applying the theoretical machinery of contemporary machine learning to stochastic and nonlinear time series analysis methods, where the time series arise from practical, real-world problems. The position is available to start in 2013 (subject to negotiation) Financial Support This studentship includes a fee bursary to cover the home/EU fees rate plus a maintenance allowance of £13,590. Applicants from outside the EU may apply for this studentship but will need to pay the difference between the ‘Home/EU’ and the ‘Overseas’ tuition fees, currently this is £10,372 in 2012/13. Background of the Project Proposed topics would include contemporary machine learning concepts such as sparsity, nonparametric Bayes, maximum margin discrimination, and structured output learning, with both deterministic and stochastic inference approaches, organized in directed and undirected, dynamic graphical models. Potential applications could include detecting and quantifying neurological disorders using multivariate signals, noise removal from irregular measurements of the symptoms of progressive diseases, and nanopore-based next-generation DNA sequencing. Person Specification The successful applicant should have a first class or upper second class honours degree or equivalent qualification in statistics, applied mathematics, computer science, electronic engineering or (mathematical) physics. Preferred skill requirements include knowledge/experience of programming with numerical software packages such as Matlab, R, Mathematica; a track record of academic publications would be an advantage. For informal enquiries please contact Dr Max Little by email (max.little@aston.ac.uk). The online application form, reference forms and details of entry requirements, including English language are available at http://www1.aston.ac.uk/eas/research/prospective-research-students/how-to-apply/  Quoting Reference R130091 Studentship Fri, 22 Mar 2013 00:00:00 GMT