PhD Position in Computational Science; Information Theory meets Causality

University of Amsterdam Informatics Institute

Netherlands

PhD position in Computational Science: information theory meets causality

Publication date 19 December 2019
Closing date 19 January 2020
Level of education Master's degree
Hours 38 hours per week
Salary indication €2,325 to € 2,972 gross per month
Vacancy number 19-878

We seek a multi-disciplinary researcher who is capable of developing advanced analytical theory and methods for conceptualizing, discovering, and inferring causality in dynamical systems. The focus is on non-equilibrium dynamics and the envisioned theory will be based on (multivariate) information theory. Specifically, the type of causality that is to be considered is ‘nudge’ causality, i.e., studying the effects of small perturbations. This research will depart from the notion of ‘information processing’ in dynamical systems, which aims to quantify how information is stored in dynamical systems and how this information percolates and interacts to eventually generate emergent systemic behavior. The overarching goal of this PhD position is to carry over insights from the field of causality discovery and inference to the field of information processing in complex systems, and vice versa, in order to produce novel theoretical insights into the concept of nudge causality.


Project description

Complex adaptive systems typically consist of multiple variables (nodes, agents, particles) which interact in a non-linear manner through a heterogeneous network of interactions, thereby generating a non-trivial systemic emergent behavior which cannot be reduced to the dynamics of a single variable. The main idea behind information processing is that these variables can be interpreted as storing information (or memory); the interactions among variables can be seen as transmitting information from one variable to the other; and the decision of the new state of a variable based on all its interactions can be interpreted as integrating information (or information synergy). The original goal of this framework is to abstract away the mechanistic details of models (since regardless whether the variables represent neurons, birds, or molecules, the framework is solely in the language of ‘bits’) and thereby characterize emergent behaviors (e.g., tipping points, pattern formation, phase transitions) in a domain-free manner. Recently, though, the realization is growing that this concept may be related to a notion of causality. That is, if a causal interaction makes information transmit from A to B, then in what way does this transmitted information represent causal influence?

A different research field altogether is that of causal discovery, causal inference. The most classic types of analyses performed in this field regards at least one of the following: static or equilibrium states, linear interactions, and/or ‘overwhelming’ interventions. Since we are interested in complex adaptive systems this project will focus instead on non-equilibrium dynamics, non-linear interactions, and ‘underwhelming/stochastic’ (nudge) interventions. This is an atypical setting in the causality inference field. Moreover and more importantly, the fields of information processing and causality inference currently hardly overlap nor interact with each other. We see this as a missed opportunity since the two fields can learn from each other, potentially leading to ground-breaking new insights.

Relevant references:

  1. https://arxiv.org/pdf/1603.03552.pdf
  2. www.hindawi.com/journals/complexity/2018/6047846/
  3. http://rsif.royalsocietypublishing.org/content/10/88/20130568
  4. http://www.mdpi.com/1099-4300/19/2/85
  5. https://www.nature.com/articles/s41467-019-10105-3


What are you going to do?

We expect from you:

  • strong analytical skills as well as computational science skills;
  • an interdisciplinary mindset and an open and proactive personality in interacting with researchers from different disciplines;
  • taking part in ongoing educational activities, such as assisting in a course and/or guiding student thesis projects, at the B.Sc. or M.Sc. level;
  • to publish high quality research articles in respected, peer-reviewed scientific journals;
  • to proactively seek collaboration with other researchers, such as from the causal discovery domain or from potential application domains.


What do we require?

  • An M.Sc. degree in Computational Science, (Applied) Mathematics, Statistics, Statistical Physics, or a closely related field;
  • a strong scientific interest in understanding complex adaptive systems at large, and the concepts of causality and information processing in particular;
  • familiarity with Shannon information theory (pre);
  • familiarity with causal discovery and inference concepts and techniques (pre);
  • experience with mathematical/computational modelling and simulating of dynamical systems;
  • proficiency with probability theory and statistics;
  • fluent proficiency in English, both written and spoken.


Our offer

A temporary contract for 38 hours per week for the duration of 4 years (initial appointment will be for a period of 18 months and after a satisfactory evaluation the contract will be extended for 30 months) and should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students.

The salary, depending on relevant experience before the beginning of the employment contract, will be €2,325 to €2,972 (scale P) gross per month, based on fulltime (38 hours a week), excluding 8% holiday allowance and an 8.3% end-of-year bonus. A favorable tax agreement, the '30% rule', may apply to non-Dutch applicants. The Collective Labour Agreement of Dutch Universities is applicable.

Are you curious about our extensive package of secondary employment benefits? Then find out more about working at the Faculty of Science.


Questions?

Do you have questions about this vacancy? Or do you want to know more about our organisation? Please contact:

  • Dr Rick Quax , Assistant professor of Information Processing in Complex Systems
    T: +31 (0)20 525 7530 


About the Faculty of Science

The Faculty of Science has a student body of around 6,500, as well as 1,600 members of staff working in education, research or support services. Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works, be it elementary particles, the birth of the universe or the functioning of the brain.

The mission of the Informatics Institute is to perform curiosity-driven and use-inspired fundamental research in Computer Science. The main research themes are Artificial Intelligence, Computational Science and Systems and Network Engineering. Our research involves complex information systems at large, with a focus on collaborative, data driven, computational and intelligent systems, all with a strong interactive component.


Job application

The UvA is an equal-opportunity employer. We prioritise diversity and are committed to creating an inclusive environment for everyone. We value a spirit of enquiry and perseverance, provide the space to keep asking questions, and promote a culture of curiosity and creativity.

The Informatics Institute strives for a better gender balance in its staff. We therefore strongly encourage women to apply for this position.

Do you recognize yourself in the job profile? Then we look forward to receiving your CV and cover letter by 19 January 2020. You may apply online by using the button below.

Applications should include:

  • a curriculum vitae;
  • a motivation letter (max 1 page);
  • your M.Sc. thesis document;
  • your M.Sc. transcript including courses and grades;
  • the names and contact details for two referees, one of whom is preferably your M.Sc. thesis daily supervisor.

We intend to invite potential candidates for interviews on 27 January 2020. #LI-DNP

No agencies please

Apply now


In your application, please refer to Polytechnicpositions.com

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