Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
signaling models”. The scholarship is full-time for 2 years, with access starting in May 2026 or by agreement. The research will be carried out in the laboratory of Cemal Erdem at the Department of Medical
-
systems or smart buildings, such as regression, classification, time series analysis, or basic predictive modelling. Experience with data handling, including data cleaning, transformation, exploratory
-
to develop multimodal deep learning models for predicting prostate cancer aggressiveness. Specifically, digital pathology images and magnetic resonance (MR) imaging will be integrated with clinical data
-
of the project is to reconstruct the environmental, biological, and societal drivers behind plague outbreaks in Eurasia between 1300 and 1900 CE. A short description of the project can be found here: https
-
digital twins be used to provide on-line predictions as to the future expected evolution of these critical properties as the basis for safe reinforcement learning (RL) for on-line optimal control”. In
-
Intelligent Control Systems RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis
-
or intervention strategies are lacking, urging the need for new perspectives on pathogen control. Within this project these perspectives will be explored. To predict correlates of disease against these complex
-
Algal Bloom Modeling and Remote Sensing. The successful candidate will apply state-of-the-art techniques to develop remote-sensing and statistical models for understanding and prediction of harmful algae
-
defects, are currently a major limiting factor for metal printing. In nanomedicine, various nanoparticles are used for controlled drug delivery and therapies, and laser-excited nanobubble-inducing shockwave
-
project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties