PhD Fellow in Data-Driven Mathematical Modelling of Ecohydrological Systems (m/f)

Updated: 2 days ago
Deadline: 15 Jul 2019

Research Direction:

This fully-funded position has significant scope for the candidate to shape their own research directions around the topic of data-driven mathematical modelling in ecohydrological systems. The broader context of the project is to improve modelling of plant ecosystems in order to make improved predictions in the context of rapidly changing environments (urbanisation, global warming and natural ecological processes).

Possible research directions for the candidate include, but are not limited to:

  • Bayesian model comparison to quantify the effects of physical laws, calibration, extremum principles and information theoretic approaches on eco-hydrological model precision and reliability.
  • Novel extremum principles for optimal nutrient uptake based on stochastic cost measures.
  • Uncertainty quantification of root plant nutrient uptake using mixed-dimensional coupled stochastic partial differential equation models.

The PhD candidate will have access to comprehensive experimental data on water-plant ecosystems from the Luxembourg Institute of Science and Technology (LIST).

The candidate will work with and contribute to leading open-source computational modelling tools, e.g. Stan (https://stanmc.org ), pymc3 (https://docs.pymc.io/ ), pymatern, FEniCS Project (https://fenicsproject.org ) and Renku (https://github.com/SwissDataScienceCenter/renku ).

Supervision:

Your lead supervisor will be Jack S. Hale (University of Luxembourg, PDEs, Numerical methods, Continuum Modelling, Computational Sciences). Further supervision will be provided by Stan Schymanski (Luxembourg Institute of Science and Technology, Experimental and Conceptual Modelling in Ecohydrology, optimality principles) and Christophe Ley (University of Ghent, Bayesian Statistics, Applied Probability).

You will be working as part of DRIVEN Doctoral Training Unit (DTU) funded by the FNR PRIDE scheme. The Computational and Data DRIVEN Science DTU will train a cohort of 19 Doctoral Candidates who will develop data-driven modelling approaches common to a number of applications strategic to the Luxembourgish Research Area and Luxembourg’s Smart Specialisation Strategies . DRIVEN will build a bridge between state-of-the-art data driven modelling approaches and particular application domains, including Computational Physics and Engineering Sciences, Computational Biology and Life Sciences, and Computational Behavioural and Social Sciences.


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