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Proposed PhD thesis topics at DP DOCC

Atmospheric science

Admission requirement: master's degree in atmospheric science or meteorology (or equivalent).

Atmospheric turbulence modelling (ATS-1)

Weather and climate models rely on a closure for sub-grid scale turbulence, which is often based on Turbulence Kinetic Energy (TKE). Recent results on TKE dissipation from non-ideal (i.e. real) terrain suggest that revisions in model parameterizations are needed. This will be investigated using Large-Eddy (and/or Direct Numerical) Simulation.

Supervisor: Rotach; Co-supervisor: Kendl/Rauch.

Development of a stochastic atmospheric boundary layer parameterization for atmospheric modeling (ATS-2)

The atmosphere is characterized by deterministic chaos, which is in numerical weather prediction and climate modeling exploited by using so-called Ensemble Prediction Systems (EPSs): many simulations with slightly perturbed initial conditions are run in order to not only obtain the most likely future state but also its uncertainty. Essentially, all the established ‘perturbation schemes’ are for the synoptic (weather) scale, so that for the present project a small-scale (boundary layer) perturbation scheme shall be explored.

Supervisor: Rotach; Co-supervisor: Haltmeier/Kendl.

Astrophysics

Admission requirement: master's degree in physics or astrophysics (or equivalent).

Modelling of cosmic-ray transport and gamma ray emission in a dynamical galaxy (APH-1)

Development of a dynamical model for Galactic cosmic-ray transport. With convergence time scales in the order of 107 years and electron-loss time scales in the order of years, corresponding numerical models require development of efficient time-integration schemes, including implicit time integrators and possibly local time-stepping schemes.

Supervisor: Kissmann; Co-supervisor: Einkemmer/Ostermann.

Implications of the three-dimensional gas distribution for galactic cosmic-ray transport (APH-2)

Determination of the three-dimensional Galactic gas distribution using probabilistic information field theory algorithms. Application of resulting gas distribution, together with statistical confidence intervals, in numerical Galactic cosmic-ray transport models with application to gamma-ray emission.

Supervisor: Kissmann; Co-supervisor: Kendl/Reimer.

Astro- and particle physics

Admission requirement: master's degree in physics or astrophysics (or equivalent).

Multimessenger astrophysics of high-energy sources (APP-1)

This project develops emission models of candidate cosmic-ray sources to predict the spectral evolution of cosmic rays, photons and neutrinos following in-source acceleration of nuclei. Including extensive nuclear reaction networks into the system of transport equations to be solved requires development of elaborate methods.

Supervisor: Reimer; Co-supervisor: Einkemmer/Kissmann.

Modelling of galactic cosmic-ray flux from colliding wind massive binary systems (APP-2)

Colliding wind massive binary (CWMB) systems have recently entered the gamma-ray regime, but their role as a contributor to the galactic cosmic-ray flux is an open question. This project develops emission models of CWMB systems with emphasis on the in-source nuclear cosmic ray flux evolution and escape. The photon output will be used with corresponding observations to constrain model parameters, by solving the transport equation considering all relevant interactions related to relativistic nuclei (including relevant nuclear reaction networks) and electron injection.

Supervisor: Reimer; Co-supervisor: Einkemmer/Kissmann.

Biomolecular chemistry

Admission requirement: master's degree in chemistry (or equivalent).

Reweighting and regularisation for accelerated biomolecular dynamics simulations (BCH-1)

Biomolecular processes occur on time scales not yet accessible by conventional molecular dynamics simulations. Different algorithms have been developed that modify the energy landscape to access longer time scales. However, after modification of the energy landscape, probabilities of the conformations have to be reweighted to regain a realistic ensemble. Most existing algorithms for reweighting suffer from large errors resulting in distorted probability distributions. In this project we will develop an alternative and more reliable solution to this problem based on Tikhonov type functionals.

Supervisor: Liedl; Co-supervisor: Haltmeier/Probst.

Deep Learning in Analysis of Molecular Dynamics Simulations (BCH-2)

Molecular dynamics simulations result in large amounts of data. Thus, state of the art methods relying on information theory and stochastics need to be developed and optimized to describe properties like hydration and aggregation. Pattern recognition of electrostatic and hydrophobic properties on complex surfaces will be applied using cutting-edge machine learning techniques.

Supervisor: Liedl; Co-supervisor: Haltmeier.

Civil engineering

Admission requirement: master's degree in civil or environmental engineering science (or equivalent).

Lagrangian microscopic biokinetic model (CEN-1)

Application of Lagrangian based CFD methods in urban water management (such as smoothed particle hydrodynamics) is a relatively recent method which allows for a novel treatment of biochemical processes in the water phase. The thesis aims to couple the flow simulation with a microscopic description of biokinetic conversion, based on a stochastic distribution of particles representing microbial flocs. This will allow to decouple fluid and particle phase in the numerical description of biokinetic processes in urban water systems.

Supervisor: Rauch; Co-supervisor: Franosch/Rotach.

Lagrangian sewer solids transport model (CEN-2)

The transport of solids in sewers is usually simulated by one-dimensional Navier-Stokes equations coupled with simplified transport models. Despite significant research efforts these models show limited predictive capabilities for special problems like clogging by textile materials or FOG (fat, oil and grease). Contrarily, this thesis should apply a Lagrangian computational fluid dynamics method, i.e. smoothed particle hydrodynamics (SPH), for establishing a multiphase (water, gas and particles) model of the sewer. The multiphase SPH model will be coupled with transport models for special objects like textiles and buildup / erosion of FOG deposits.

Supervisor: Rauch; Co-supervisor: Franosch/Harders.

Engineering

Admission requirement: master's degree in civil or environmental engineering science (or equivalent).

3D simulation of deep tunnel advance with interactions between multiple tubes (ENG-1)

Development of 3D time-dependent numerical models of deep tunnel advance with a focus on the challenging task of interactions between several tubes. For this purpose advanced constitutive models for rock mass and shotcrete and regularization techniques for material softening, beyond the capabilities of standard material models are required.

Supervisor: Hofstetter; Co-supervisor: Lackner/Ostermann.

Modeling of existing and emerging discontinuities in a rock mass with applications to numerical simulations of tunneling (ENG-2)

Rock mass is composed of intact rock and discontinuities, e.g. bedding planes and joints. The latter are already present in the prevailing in-situ conditions. However, discontinuities may also emerge from the stress changes in the rock mass due to tunnel advance. The aim of the thesis is to evaluate different approaches of modeling existing and emerging discontinuities in rock mass with special emphasis on applications to numerical simulations of tunnel advance.

Supervisor: Hofstetter; Co-supervisor: Lackner/Ostermann.

Constitutive modeling of orthotropic rock (ENG-3)

The thesis project comprises the development of a constitutive model for describing the nonlinear mechanical behavior of orthotropic rock subjected to 3D stress states, the implementation of the model in a finite element code and the validation of the model by numerical simulations of laboratory experiments. In particular, the model must be able to represent irreversible deformations, associated with strain hardening and strain softening as well as degradation of stiffness.

Supervisor: Hofstetter; Co-supervisor: Lackner/Ostermann.

Mathematics

Admission requirement: master's degree in mathematics (or equivalent).

Dynamic low-rank approximations for kinetic models in plasma physics (MAT-1)

Solving kinetic problems directly is extremely expensive from a computational point of view. We consider the recently developed dynamic low-rank approximation, which has the potential to reduce the required effort by orders of magnitudes. We focus on developing algorithms and their implementation on HPC systems in the context of problems in plasma physics.

Supervisor: Einkemmer; Co-supervisor: Kendl/Reimer.

Semi-Lagrangian plasma simulation on modern computer architectures (MAT-2)

Large scale simulations on supercomputers are usually required to solve the various models for the nonlinear dynamics of magnetized fusion plasmas. Most of the algorithms currently available, however, do not fit very well to modern computer architectures (for example, GPUs). One approach to overcome this limitation are semi-Lagrangian discontinuous Galerkin methods.
We will, in particular, further develop these algorithms, implement them on HPC systems, and demonstrate their efficiency for plasma simulation.

Supervisor: Einkemmer; Co-supervisor: Kendl/Ostermann.

Dynamic tomography of complex continua (MAT-3)

Dynamic tomography allows real-time imaging of many physiological processes, ranging from cardiovascular imaging to non-invasive surgery monitoring. Standard recovery methods accounting for rapid movements are only suitable for simple rigid motion. We consider efficient image reconstruction for complex motions, using tools from regularization theory, inverse problems, deep learning and neural networks to integrate suitable a-priori information.

Supervisor: Haltmeier; Co-supervisor: Hofstetter/Probst.

NETT deep learning for time dependent inverse problems (MAT-4)

Inverse problems arise in various applications ranging from medical imaging to non-destructive testing and remote sensing. Their characteristic feature is the inherent ill-posedness, requiring special techniques for its solution. We recently proposed network Tikhonov regularization (NETT) for static inverse problems, which is based on generalized Tikhonov regularization using a neural network as learned regularizer. The aim of this project is to extend the NETT to dynamic inverse problems. In particular, appropriate networks and training strategies will be designed, a convergence analysis developed and an efficient numerical implementation established.

Supervisor: Haltmeier; Co-supervisor: Kendl/Ostermann.

Advanced time integration schemes (MAT-5)

Modelling complex continua results in PDEs exhibiting high oscillations, loss of regularity and nontrivial boundary conditions. Standard integrators typically fail in such situations and give unreliable solutions. Based on our recently developed exponential integrators and splitting methods, the thesis project aims at constructing integrators that address this challenge. The new methods will be developed in close coordination with the other DOCC projects.

Supervisor: Ostermann; Co-supervisor: Franosch/Kendl/Kissmann.

Exponential integrators for nonlinear advection-diffusion problems (MAT-6)

The goal of this project is to construct and analyze a new class of exponential-type integrators, particularly designed for the time integration of nonlinear advection-dominated problems. Their construction will be based on the nonlinear variation of constants formula. We will analyze stability and convergence, and study the effect of non-trivial boundary conditions on the rate of convergence. The implementation of the method requires the action of a nonlinear flow, which is typically provided with the help of a semi-Lagrangian approach.

Supervisor: Ostermann; Co-supervisor: Franosch/Kendl/Kissmann.

Medical computing

Admission requirement: master's degree in computer science (or equivalent).

Machine learning for tuning parallel computations in surgical simulation (MEC-1)

Simulators for computer-based surgical training comprise heterogeneous, computationally expensive components, running at fast update rates. We focus on techniques capable of autotuning for time-critical complex biomechanical simulations on parallel systems. A key difficulty is the dynamically changing requirements of simulation components, e.g. due to cutting or interaction.

Supervisor: Harders; Co-supervisor: Einkemmer/Lackner.

Acceleration of physically-based simulations with convolutional neural networks (MEC-2)

Machine learning methods have proven to be very efficient for approximating nonlinear functions, if an accurate and large enough dataset is provided for training. We examine if physically-based simulations, such as deformation or fluid flow computations, can be accelerated via machine learning, specifically using convolutional neural networks. Special focus will be on error tracking, prediction, and correction in adaptive methods for particle-based solvers.

Supervisor: Harders; Co-supervisor: Rauch.

Nanophysics

Admission requirement: master's degree in physics (or equivalent).

Structure formation and instabilities in highly-charged nano droplets (NPH-1)

This thesis project accompanies planned laboratory experiments in the Scheier group on formation and properties of highly-charged Helium nano droplets by modelling and simulation of formation of Coulomb crystals, their (in-)stability, and interactions with quantised vortices and neutral dopants, combining methods from molecular, fluid and plasma dynamics.

Supervisor: Kendl; Co-supervisor: Einkemmer/Scheier.

Multicenter growth processes of nano-clusters in suprafluid Helium (NPH-2)

Pickup of individual atoms or molecules into superfluid He nanodroplets leads to formation of clusters, nanoparticles and wires. When initially ionized, charge centers will act as seeds for cluster growth. In large droplets containing many charge centers, Coulomb repulsion keeps them apart at maximum distances, resulting in a uniform distribution of highly attractive nucleation seeds. Thus homogeneous cluster growth is expected around each charge, and confirmed by first experiments. In this project we plan to simulate such multicenter growth processes via classical and quantum molecular dynamics simulation with the aim to understand the basic molecular processes. This can be utilized to optimize particular structures, such as core-shell or Janus particles. Predictions derived from these models will be compared with experimental studies.

Supervisor: Scheier; Co-supervisor: Kendl/Probst.

Nano- and Biophysics

Admission requirement: master's degree in physics (or equivalent).

Non-equilibrium dynamics of colloidal suspensions under strong external driving (NBP-1)

We will simulate the nonlinear dynamics of a colloidal suspension in response to an external perturbation in the form of a strong step-strain, and elucidate the evolution of shear stresses. New algorithms are needed to subtract thermal noises of the non-interacting systems for optimization of the signal-to-noise ratio.

Supervisor: Franosch; Co-supervisor: Adam/Kendl.

Disentangling the noise from the interactions in Brownian Dynamics (NBP-2)

In a conventional Brownian Dynamics simulation interacting particles undergo an erratic motion due to thermal noise between collisions. A novel algorithm will be elaborated that allows to directly simulate only the difference between an interacting and freely evolving system thereby basically eliminating all noise and making the relevant physics accessible to simulation studies.

Supervisor: Franosch; Co-supervisor: Adam/Kendl.

Material physics and chemistry

Admission requirement: master's degree in physics (or equivalent).

Machine learning methods for advanced material simulations (MCH-1)

Modern materials science makes heavy use of atomistic simulations to predict and optimize compounds. Dynamical properties, for example degradation, self-diffusion, atom migration at surfaces and reconstruction are, however, not directly accessible on this level. We want to find out how information gained by atomistic simulations can be extracted so that also slow processes or new features can be modelled. Besides established methods like kinetic Monte Carlo, it shall be explored at which level machine learning algorithms might be best applied to this aim.

Supervisor: Probst; Co-supervisor: Haltmeier/Liedl.

Dynamics of molecules in the plasma / surface region (MCH-2)

We want to predict the dynamic interactions of plasma components with respect to each other and to the material and energy exchange near a surface. Important interactions of the first type are electron-impact excitation, ionization and photoemission from excited states, which are simulated by empirical and quantum chemical modelling. The second types are governed by sputtering and diffusion. They can be studied by molecular dynamics, also on an empirical level or by direct dynamics. This project shall improve our numerical models for plasma-surface interactions.

Supervisor: Probst; Co-supervisor: Haltmeier/Ostermann.

Material sciences

Admission requirement: master's degree in material science, civil engineering science or physics (or equivalent).

Multiscale framework for hierarchically-organized protective materials (MSC-1)

Impact processes take place frequently in daily life with e.g. protective materials reducing the severity of the occurring impact. In this project, the performance of protective materials shall be related to their microstructure employing a multiscale framework, upscaling information from the finer scales towards the macroscale, and finally enabling simulation of the compaction behaviour of protective materials when subjected to impact loading.

Supervisor: Lackner; Co-supervisor: Harders/Hofstetter.

Pore-space specific modeling of injection processes (MSC-2)

The injection process of viscous, chemo-mechanical (i.e. hardening) fluids into porous materials shall be modelled and simulated employing the smoothed particle hydrodynamics method.
The underlying approach accounts for the ongoing hardening reaction during the injection, resulting in a temperature rise and hence heat flow. By considering realistic pore-space geometries generated by computer tomography the realistic simulation of strength-increasing injections as e.g. performed in medical applications (injection of PMMA into porous bone) becomes possible.

Supervisor: Lackner; Co-supervisor: Harders/Rauch.

Structural engineering

Admission requirement: master's degree in civil engineering science (or equivalent).

Reliability analysis of high-speed railway bridges (SEN-1)

Reliability analysis of railway bridges subjected to high-speed trains by application of non- standard stochastic methods such as subset simulation or line-sampling with small estimator variances; Novel elaborate numerical modeling of dynamic vehicle-bridge-track-subsoil interaction; Application of high-performance computing.

Supervisor: Adam; Co-supervisor: Haltmeier/Rauch.

Effect of soil-structure interaction on recorded vertical ground motion components (SEN-2)

Recent numerical studies have shown that earthquake excited regular steel-moment-resisting frames do not behave rigid in vertical direction, but show a significant peak acceleration response amplification. However, simulations based on recorded vertical free-field ground motions may overestimate this amplification. Aims of this project are to evaluate the impact of soil-structure interaction on recorded ground motion, develop soil-structure interaction models for HPC simulations, and derive seismic response on the free-field surface and at the base of the structure.

Supervisor: Adam; Co-supervisor: Hofstetter/Rauch.



Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847476.

Co-funded by the European Union

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