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      <img style="width:100%; min-width: 300px; max-width: 500px; display: block; margin-left: auto; margin-right: auto;" src="/static/tiago/sbm.png" alt="SBM"/>
      <div style="text-align: center; font-size: smaller;">
        (Modular structure of a <a href="https://en.wikipedia.org/wiki/Food_web">food web</a>, extracted with the method described <a href="https://arxiv.org/abs/1708.01432">in this paper</a>.)
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      <a href="http://www.bath.ac.uk/math-sci/"><img style="width: 300px; display: block; margin-left: auto; margin-right: auto;" src="/static/tiago/uob-logo-grey-transparent-1.png" alt="Uni Bath"/></a>
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    <h1>PhD position: “Inferring the evolutionary forces shaping the structure and function of complex network systems”</h1>
    <h2 style="margin-top:0em;"><a href="http://www.bath.ac.uk/math-sci/">Department of Mathematical Sciences</a>, <a href="http://www.bath.ac.uk">University of Bath</a>, UK</h2>
    <h2 style="margin-top:0em;"><a href="http://www.bath.ac.uk/research/centres/cncb/">Centre for Networks and Collective Behaviour</a></h2>
    <h2>Supervisor: <a href="/tiago">Dr. Tiago P. Peixoto</a></h2>
    <h3>Application deadline: <b>November 30th, 2017</b>, <a href="http://www.bath.ac.uk/science/postgraduate-study/research-programmes/how-to-apply/" style="background-color: rgb(255, 255, 128);">Online application process</a><br/> <span style="font-size: smaller">[choose:
Department of Mathematical Sciences/PhD programme in Mathematics (full-time)]</span>

    </h3>

    <h2> Project description </h2>
    
    <p>An enormous variety
    of <a href="https://en.wikipedia.org/wiki/Complex_system">complex
    systems</a> shares the unifying property that they can be
    mathematically modelled as
    a <a href="https://en.wikipedia.org/wiki/Network_science">network of
    interacting elements</a>. Examples of this include social
    iterations, communication systems, cell metabolism, transportation
    infrastructure, among many others. Despite the different domains,
    all these systems can be modelled at their most fundamental level
    under the same network formalism. With the aim of exploiting this
    universality, a great deal of transdisciplinary research has been
    devoted to developing general network models that are valid across
    different domains.

    <p>The aim of this PhD project is to move towards this goal using
    a specific blend of mathematical modelling and data analysis,
    based heavily on concepts and analytical tools
    from <a href="https://en.wikipedia.org/wiki/Statistical_physics">Statistical
      Physics</a>, and employing a variety of approaches
    from <a href="https://en.wikipedia.org/wiki/Bayesian_inference">Bayesian
      Inference</a>
    and <a href="https://en.wikipedia.org/wiki/Machine_learning">Machine
      Learning</a>. In particular, the main objectives are:

    <ol>
      <li> Elaboration of generative models of networks that take into account
        key evolutionary aspects (e.g. optimization towards robustness under
        constraints, homophily, incremental growth dynamics), and yield
        credible descriptors of large-scale network structure (e.g. modular
        organization, hierarchies and centralization).
      </li>

      <li>Development of <a href="https://graph-tool.skewed.de/static/doc/demos/inference/inference.html">principled
          inference methods</a>
        that can extract model parameters from real-world network data
        via efficient algorithms, as well as model selection
        approaches that can identify the most appropriate generative
        process based on empirical evidence.
      </li>

      <li>Employment of the modelling and inference frameworks to make
        predictions that generalise from past observations, identify errors
        and omissions in data, as well as opportunities for architectural
        improvements.
      </li>
    </ol>

    <p>The combination of these three objectives would yield concrete
    connections between the structure, function and evolution of network
    systems, with potential applications as diverse as preventing the
    outbreak of diseases and traffic jams, discovering new interactions
    between drugs, and building a censorship-free internet.

    <p>Furthermore, the diverse and multidisciplinary nature of the research
    would give the candidate many options in further pursuing an academic
    career in Theoretical Physics, Machine Learning and Data Science, as
    well as opportunities for applications in industry.

    <p>The successful candidate should be highly motivated and have a degree in
    Physics, Applied Mathematics or related fields. Demonstrable familiarity
    with mathematical modelling as well as computational skills
    (C/C++ and/or Python) is essential.
    
    <p>The position is for 3.5 years of full-time study and will
    administratively belong to the Department of Mathematical Sciences
    at the University of Bath, associated with the Centre for Networks
    and Collective Behaviour, and will be supervised
    by <a href="/tiago">Dr. Tiago Peixoto</a>.

    <p>The application deadline is <b>November 30th, 2017</b>, and the
    successful candidate will be ready to start by <b>March 2018</b> at
    the latest. Applications should be done online,
    via <a href="http://www.bath.ac.uk/science/postgraduate-study/research-programmes/how-to-apply/"
    style="background-color: rgb(255, 255, 128);">the Doctoral
    College</a>. Choose “Department of Mathematical Sciences/PhD
    programme in Mathematics (full-time)” and please mention the name of
    the project and supervisor in your application. Informal
    inquiries should be directed to <a href="/tiago">Dr. Tiago
    Peixoto</a>.

    <p>Applications may close early if a suitable candidate is found;
    therefore, early application is recommended.

    <h4>Funding Notes</h4>

    <p>EU students applying for this project may be considered for a
    University Research Studentship which will cover UK/EU tuition fees, a
    training support fee of £1000 per annum and a tax-free maintenance
    allowance of £14,296 (2016/17 rate) for 3.5 years.

    <p>Note: ONLY EU applicants are eligible for the studentship;
    unfortunately, applicants who are classed as Overseas for fee paying
    purposes are NOT eligible for funding.

    <p style="background-color: rgb(135, 206, 250); padding:.5em"> Those
    interested in this project should consider <b>also</b> submitting a
    separate application for a PhD scholarship offered by
    the <a href="http://www.bath.ac.uk/math-sci/postgraduate/samba/programme/">SAMBa
    CDT</a>. Selection to this programme would also give you the
    opportunity to work on similar projects in Tiago Peixoto's group.

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