Sort by
Refine Your Search
-
available in the further tabs (e.g. “Application requirements”). Objective This scholarship programme offers you the opportunity to continue your education in Germany with a postgraduate or continuing course
-
, offering a rare opportunity to develop skills that are highly transferable to academia, industry, and large-scale research infrastructures. Further development of your personal strengths, e.g. via a
-
Your Job: We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
-
of Economics and Business Administration with opportunities to complete a Master's degree course at a state (public) or state-recognised German higher education institution and to gain a Master's Degree in
-
a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop 3D+t image reconstruction methods in a cell microscopy setting using image sequences
-
a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop physics-aware simulations of growing cell populations, including their spatiotemporal
-
particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design
-
modelling and the land-surface model used in the project. Develop simplified, fast-running model surrogates using machine-learning methods to replace very time-intensive simulations. Design an efficient
-
based techniques to analyse spike train recordings to advance our understanding of neural population coding while maintaining clarity in the interpretation of results. Concurrently, AI-based methods
-
Infrastructure? No Offer Description Work group: IBG-4 - Bioinformatik Area of research: PHD Thesis Job description: Your Job: Develop methods and workflows to construct robust co-regulation networks from large