PostDocs

The goal of this project is to contribute to the development of a new generation of human-computer interfaces that pro-actively adapt to users’ future actions by predicting their interaction intentions. Specifically, the project will formulate and study intent prediction in a fully Bayesian framework. This approach will not only enable future interfaces to quantify and adapt to the inherent uncertainty of the intent predictions themselves, but will also leverage this information to improve subsequent predictions of users’ actions.

The goal of the project "Designing aqueous deep eutectic mixtures by data-integrated simulation" is to develop and apply a design strategy for deep eutectic solvents by combining molecular dynamics simulations and machine learning strategies. Deep eutectic solvents (DES) are promising solvents in biocatalysis, because they are designable, renewable, biodegradable, and cheap.

The goal of this interdisciplinary project is to develop approaches for critically reflecting upon potential effects and risks of research in simulation science, including (but not limited to) ethical or social concerns. Building on common methodology from candidates' roots disciplines, these can span a variety of tools, models or media to assess issues related to data ethics, privacy, security, dual use or other relevant topics.

The ability to autonomously perform activities of daily living (ADL), e.g., during a meal where food has to be cut or where a glass has to be filled from a bottle, is crucial for self-determination and quality of life in patients with neuro-degenerative diseases, such as Parkinson’s disease, multiple sclerosis or cerebellar ataxia. Although these patients are able to plan motor actions, tremor or overshooting movements disturb the intended movements. Assistive devices that pro-actively suppress such dysfunctional movement components could dramatically improve patients’ motor abilities. However, predicting planned movements during ADL remains profoundly challenging. The goal of this research project is to pioneer the first non-invasive approach to suppress unwanted (dysfunctional) movement while allowing intended movement. This requires computational methods to predict human arm movement intentions and execution from multi-modal bio-signals stemming from real and synthetic data during everyday tasks. To this end, the project aims to combine eye movement recordings, multi-modal arm motion recordings, and novel machine learning techniques with a biophysical neuro-musculoskeletal human model to predict the next, most-likely intended user action. As such, the proposed project will contribute new methods to generate control signals for the neuro-musculoskeletal computer simulations and complement ongoing efforts by studying intent prediction during motor actions executed as part of ADL.

Autonomous driving increases the risk of injury during a crash due to changed occupant positions (e.g. rotation of the head and trunk) and the distraction of occupants from the driving situation (e.g. by conference calls). This project aims to enhance the existing human models for crash simulations by implementing realistic head and body rotations. Therefore, experiments (muscle activation via EMG, 3D motion analysis) in a driver-in-the-loop setup (driving simulator) will be performed to determine model parameters and to validate the human body model. Based on these data, the influence of head rotation on injury risk during accidents will be assessed in silico by human model simulations (digital human twin).

Current global climate developments suggest that extreme weather conditions will occur more often and more intense than in the past. One example of such severe conditions are heat waves, which will strongly influence life in big cities. Buildings and pavements store heat during the day and release it during the night, leading to constantly high temperatures without periods of cooling in between. Plants can help to reduce the impact of heat waves in big cities due to evaporative cooling. In this project we will focuses on the description of evapotranspiration processes, i. e. the transport of water, gas and energy in the direct environment of a plant leaf. In a first step, the leaf is described in a static manner. In the second step, leaf motion in the wind field as well as the influence of radiation are taken into account. Cooperations with the University of Ulm, the ETH Zurich and different partners form SimTech will allow to describe the leaf structures in detail, which is a prerequisite to model leaves as a porous medium.