Open positions

Post-Doctoral Position

Deadline for Applications has passed!

Discrete Bifurcation Analysis

We are seeking a candidate for a postdoctoral research position (one year with the posibility of an extension to the second year after a review). The successful applicant will carry out his/her postdoc research in the System Biology Laboratory under supervision of prof. Lubos Brim.

Task description

Continuous dynamical systems mostly contain certain kinds of parameters. It can happen that a slight variation in a parameter can have a significant impact on the systems dynamics. Finding critical conditions for such transitions is typically rephrased as finding the parameter values at which bifurcations exist. The goal of bifurcation analysis is to produce parameter space maps or bifurcation diagrams that divide the parameter space into regions of topologically equivalent systems. When applying formal methods we usually suppose a discretised version of the dynamical system under consideration and perform a discrete bifurcation analysis.

The goal of the research is the development of new original computational methods that would allow to use formal verification techniques, model checking in particular, as a vehicle for performing dicrete bifurcation analysis. Scalability needed for the analysis of complex (multi-dimensional and multi-scale) models is to be supported by using techniques as known from high-performance computing, e.g. parallel and distributed model checking algorithms.

Conditions and requirements

Gross salary is 50,000 CZK per month which, with an optional 10% bonus, sums to more than 24,000 EUR per year. Additional funds of 4,000 EUR per year will be available for travel and material expenses. Expected start date of the contract is September 1, 2016, but a later start date may be negotiated.

Candidates must have a Ph.D. degree not older than 4 years at the time of application, from a university outside of the Czech and Slovak Republics. In case that the Ph.D. defense is not yet finished, the candidate must also provide an official letter certifying that his/her Ph.D. thesis has already been submitted for defense and outlining the expected schedule of the Ph.D. defense. Candidates with a Ph.D. degree from a Czech or Slovak university may also be considered if they prove at least two years of post-doctoral research experience abroad.

Contact details

For further information and enquiries about this post please contact Lubos Brim.

Doctoral Research Fellow - PhD

The position has been filled!

Parameter and model fitting of complex adaptive systems

Supervisor: prof. Lubos Brim

Many natural systems (e.g., brains, immune systems, ecologies, societies) and increasingly, many artificial systems (parallel and distributed computing systems, artificial intelligence systems, artificial neural networks, evolutionary programs) are characterized by apparently complex behaviours that emerge as a result of often non-linear spatio-temporal interactions among a large number of component systems at different levels of organization. These systems have recently become known as Complex Adaptive Systems (CAS). Building suitable sound integrated dynamic models of CAS can be seen as a key step towards the development of predictive models of whole systems. While the structure of such models is usually available, some of the quantitative features of models are not easily determined. These quantitative attributes, which significantly affect the system dynamics, are usually reflected in the model as parameters. In order to obtain reliable models, parameters, such as reaction rates or concentration values, need to be specified exactly hand in hand with appropriate modifications in the model structure. Parameter and model fitting is an algorithmic procedure which seeks to find model parameters and model structure adaptations which make the results computed from a model agree with a dataset obtained by measurements and observations of the system or by temporal pattern hypotheses about the system dynamics provided by analysts. The goal of this dissertation project is the development of new original computational methods that would allow to use formal verification techniques, model checking in particular, as a vehicle for discovering parameter values and model adaptations of discrete models that guarantee the system satisfies the given global property (hypothesis) formulated in temporal logic. The approach will be based on verification as this is in principle an excellent methodology to verify/refute interesting hypotheses. Scalability needed for the analysis of complex (multi-dimensional and multi-scale) models is to be supported by using techniques as known from high-performance computing, e.g., parallel and distributed model checking algorithms.

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