Vacancies

This is a joint announcement for doctoral and post-doctoral positions

PhD/PostDoc positions

The Cluster of Excellence "Data-Integrated Simulation Science" (EXC 2075) is an interdisciplinary research center with more than 200 scientists performing research toward a common goal: We target a new class of modeling and computational methods based on available data from various sources, in order to take the usability, precision and reliability of simulations to a new level.

In the current funding round, open positions are announced in the following project networks (PN):

  • PN 1: Data-Integrated Models and Methods for Multiphase Fluid Dynamics
  • PN 2: In Silico Models of Coupled Biological Systems
  • PN 3: Data-Integrated Model Reduction for Particles and Continua
  • PN 4: Data-Integrated Control Systems Design with Guarantees
  • PN 5: On-the-fly Model Modification, Error Control, and Simulation Adaptivity
  • PN 6: Machine Learning for Simulation
  • PN 7: Adaptive Simulation and Interaction

Here you find a list of the positions:

PhD positions

Project lead:

Position:

  • 1 PhD position for 3,5 years, 100 %, TV-L E13

What we are looking for:

  • a talented and motivated candidate with a background in mathematics, simulation technology, natural sciences or related fields with strong interest in interdisciplinary work

Abstract:

Most cancer types such as colon cancer and melanoma show highly heterogeneous phenotypes across individual patients, and can ultimately only be cured by treatment strategies that eliminate cancer cells by inducing apoptotic cell death. Apoptotic signaling pathways are relatively well characterized compared to other pathways, and many key players and regulation mechanisms are known. The aim of this project is to develop novel data-driven modeling approaches to address the gap between the molecular understanding of network-coded death decisions in cancer cells and their consequences on patient outcome.

Project lead:

Positions:

  • 1 PhD position for 3,5 years, 100 %, TV-L E13

We are looking for:

  • A master degree in Biomedical Engineering, Medicine, Sport Sciences or similar
  • Basic knowledge on motor control physiology and biomechanics, signal processing and coding are strongly recommended
  • Willingness to perform experimental procedures (non- or minimally invasive) on volunteering human subjects is a fundamental prerequisite
  • The candidate should be fluent in English
  • Previous experience in clinical settings and / or human experiments (at least non-invasive procedures) is welcome but not necessary, since there will be extensive training throughout the duration of the PhD

Abstract:

This project aims at experimentally determining the influence of individual somatosensory systems on the activation of human muscles. The experiments will comprise neurophysiological as well as biomechanical measurements, that will take place at the Laboratory of Neuromechanics of SimTech.

Experimental results will be used to integrate excitatory and inhibitory contributions in a motor unit pool recruitment model, to better understand the relationship between afferent input and motor output. Individual sensory channels are experimentally targeted by means of specific perturbation paradigms, to evaluate how each of them contribute in tuning the descending drive to the muscle. Experiments will start from fully controlled contractions (i.e., isometric, on individual muscles) to complex movements (multi-muscle coordination).

Project lead:

Positions:

  • 1 PhD position for 3,5 years, 75 %, TV-L E13

We are looking for:

  • Background in biophysics, biomechanics, modelling and simulation

Abstract:

This project aims at experimentally determining, and then modelling, the influence of individual somatosensory systems on the activation of human muscles. The experimental results, which are recorded in a partner project, will be used to integrate excitatory and inhibitory contributions in a motor unit pool recruitment model, to better understand the relationship between afferent input and motor output. The individual sensory channels are experimentally targeted by means of specific perturbation paradigms to evaluate how each of them (and their combinations) contribute in tuning the descending drive to the muscle (partner project). Sensor dynamics will be predicted on the basis of an enhanced muscle spindle model and included into a biophysical system model of a human limb. Ultimately, the project strives for answering the question as to whether it becomes possible in the future to decode motion such that sensory feedback can be predicted without having the actual sensor data at hand.

Project lead:

Positions:

  • PhD student position for 3  years (with possibility of extension)

We are looking for:

  • Background in chemistry, physics, or a related discipline

Abstract:

Machine-learned (ML) surrogate models will be used to find minima, saddle points and reaction paths on potential energy surfaces of materials. These can be used to characterize the energetics and kinetics of chemical processes or the structure of materials on an atomistic scale. Optimization algorithms will be developed and adjusted to different ML approaches to deal with the inherent numerical noise in the data.

Project lead:

Positions:

  • PhD position for 3  years, TV-L E13

What we are looking for:

  • MSc graduate with a strong background in control systems, machine learning or applied mathematics

Abstract:

Modern machine learning seeks to understand the world from data, while classical control emphasizes building models from first principles. These seemingly conflicting views will be united in this project, and a framework for modeling of dynamical systems and subsequent controller synthesis will be developed that integrates first principle models and data in novel ways. The project emphasizes the development of theory which permits to provide rigorous guarantees for controlled systems, while also providing the opportunity of working with state-of-the-art autonomous robots.

Project lead:

Position:

  • 1 PhD position for 3 years, 100 %, TV-L E13

What we are looking for:

  • MSc graduate with a strong background in mathematically oriented control engineering
  • Desirable is an expertise in convex optimization for control at the interface with machine learning techniques

Abstract:

Integrating machine learning and data in the design of controllers is highly promising for mastering future complex technological systems. The goal of this project is to develop novel control synthesis methodologies that permit to exploit the benefit of data and learning on top of classical control and model structures. A particular emphasis is put on the largely open questions of how to provide rigorous stability and robustness guarantees for the overall learning-control system.

Project lead:

Position:

  • 1 PhD position for 3,5 years, 100 %, TV-L E13

We are looking for:

  • equally good background in computer science and numerical mathematics

Abstract:

Classical numerical method development and high-performance computing often target the optimization of a single efficiency aspect of a simulation such as accuracy, runtime, memory consumption or scalability. And they typically optimize isolated methodological ingredients such as grid activity, the parallelisation strategy, or the iterative solver. This unconventional project aims at quantifying and optimizing the global tradeoff over all ingredients of a simulation in a systematic way. It requires the definition of new metrics and objective funtions, optimization in high-dimensional and mixed continuous-discrete parameter spaces as well as classical numerical and high performance computing techniques. The target application is an existing muscle simulation environment.

Project lead:

Position:

  • 1 PhD position for 3 years, 75 %, TV-L E 13

What we are looking for:

  • completed master or diploma studies in mathematics, programming experience (preferable C++) and the successful candidate should have knowledge in at least one of the following subjects: optimisation,optimal control, partial differential equations, numerical analysis

Abstract:

We develop an optimisation framework that will on-the-fly identify parameters in an adaptive computational model for gas storage. These parameters might be physical unknown values of interest or indicate which type of physical model is locally used (multi-modelling). The optimisation framework consists of an optimal control problem, where the objective function contains local criteria and the fulfilment of the model equation appears as constraint.

Project lead:

Position:

  • 1 PhD position for 3,5 years, 100 %, TV-L E13

What we are looking for:

  • good programming skills for distributed and parallel systems, basic knowledge in simulation

Abstract:

The project is part of a larger group of projects that aim to develop a simulation toolkit for interactive pervasive simulations. The project particularly contributes to the hardware-specific optimization of simulation components fulfilling different user-defined constraints, e.g., in terms of runtime or accuracy. This comprises the definition
of metrics characterizing each simulation module, the implementation of different solvers for different models targeting a selection of different architectures (e.g., mobile phone, laptop, cluster), and the development of  automated scheduling tools for heterogeneous distributed systems as well as user and sensor interaction interfaces.

 

Project lead:

Position:

  • 1 PhD position for 3,5 years, 100 %, TV-L E13

We are looking for:

  • primarily computer scientists with a strong affinity to numerical mathematics

Abstract:

The project aims to develop a simulation's 'provenance', i. e. its meta.data, to enable the automatic adaptation of a simulation to a given problem instance, precision and time-to-solution in various metrics, and allow better performance predictions. Leveraging, in a systematic and innovative way, a much wider range of meta-data than in the current state of the art is crucial to bridge the gap between ad-hoc approaches in high-performance computing and hardware-agnostic black-box workflow systems.

Project lead:

Position:

  • 1 PhD position for 1,75 years (extension maybe possible), 100 %, TV-L E13

We are looking for:

  • primarily computer scientists

Abstract:

The context of this project is to allow for a simple specification of complex simulation systems, which can then be automatically distributed, deployed, and adapted to the underlying simulation system in an optimal way. To this end, methods that rest on techniques that trace simulations (their modeling, deployment, execution, and adaptation) using so-called provenance need to be developed. The goal of this project is to enable this traceability, which is the foundation to support adaptive behavior as well as reproducibility of simulation results in dynamic environments. The developed methods and data models will integrate a requirements-aware provisioning language used for the simple specifications of complex simulations.

Project lead:

Position:

  • 2 PhD positions for 3,5 years, 100 %, TV-L E13

What we are looking for:

  • Strong background in either machine learning or computer vision with an interest in applying these methods to problems in human-computer interaction, e.g. intelligent user interfaces and social signal processing

Abstract:

The goal of this project is to simulate human cognition by combining cognitive architectures, such as Adaptive Control of Thought Rational (ACT-R) or Executive-Process Interactive Control (EPIC) with data-driven learning methods. More specifically, this project will investigate data-integrated mehods to simulate the interplay between human visual attention, working and long-term memory, and interactive behaviour, with and without inclusion of physiological data, such as human gaze. We will demonstrate and empiricall evaluate the newly developed methods in an interactive pervasive simulation (augmented reality)  application.

PostDoc positions

Project lead:

Position:

  • 1 PostDoc position, for 2 years + 1 year extension possible, 100 %, TV-L E13

We are looking for:

Doctorate in aerospace engineering or physics, experience in optical metrology (PIV and others) and data analysis, excellent writing skills, fluency in English is required, command of German would be appreciated

Abstract:

This experimental project focuses on understanding how different porous structure topologies will influence turbulent fluid flow interactions. Different types of porous media will be considered with pore sizes ranging from the millimetre to the sub-micron range. The main goal is to enable measurements at different scales with a high spatial and temporal resolution, which will provide a better understanding of the turbulence pumping mechanism. Time-resoveld PIV (Particle Image Velocimetry) and PIT (Particle Image Thermometry) will be employed.

The position does not involve any teaching responsibilities.

The open positions are integrated into the Graduate School of the Cluster. This means:

  • A structured, quality-assured and interdisciplinary education programme
  • Subject-specific lectures and funded seminars for soft skills and interpersonal skills
  • International networking during a stay at research institutes abroad

We seek applicants holding an excellent Master's degree in Mathematics, Engineering or Computer Sciences (or a corresponding PhD if applicable). Successful candidates should be curiosity-driven, ambitious, creative, and passionate about interdisciplinary research in the area of simulation and data sciences.

Please send your application electronically by June 30, 2019 to application@simtech.uni-stuttgart.de. However, also later submissions will be taken into consideration.

Applications should include:

  • a cover letter,
  • an academic CV,
  • a one-page motivation text indicating your preferred project as well as up to two further projects of interest,
  • names of potential academic referees,
  • certificates and transcripts of records from your Bachelor/Master degrees,
  • language certificates (German or English if available).

Applicants for PostDoc positions must also include their PhD certificate, a publication list and PDFs of two relevant publications. For further information, please contact the Management Team via application@simtech.uni-stuttgart.de.

---

The University of Stuttgart is an equal opportunity employer. Applications from women are strongly encouraged. Severely challenged persons will be given preference in case of equal qualifications.

Selection is competitive, and funding is based on employment contracts subject to the tariff rules of the State of Baden-Wurttemberg (TVL E13).

 

 

Zum Seitenanfang