Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
site in Heidelberg, invites international students holding a Master’s degree in (molecular) biology, (bio-)chemistry, (bio-)physics, computational biology, computer science, epidemiology / public health
-
function and structure in vivo and in vitro Apply state-of-the-art methods to analyze synaptic mechanisms and neuronal structure REQUIREMENTS: Above-average Master’s degree in a relevant field Strong
-
Call for applications We are pleased to announce the call for the second cohort of our Graduate Programme RNAmed – Future Leaders in RNA-based Medicine. Applications are invited for 11 PhD
-
program specific seminars as well as an individualized curriculum comprising lab and soft-skill courses, congresses, symposia and summer schools. Communication and teaching language throughout HBRS is
-
, United Kingdom Supervisor: Dr. Ivana Savic Focus: computational materials modelling Background: physics, computational materials science. or a closely related discipline Apply: https://mgician.eu/research/doctoral
-
Job Id: 11716 Fixed term of 3 years | Full-time with 100% | Salary according to TV-L E13 | Institute of Medical Informatics We are UKM. We have a clear social mission and, with our focus
-
Pathogens, a WHO Collaborating Centre, and a member of the Leibniz Research Association. The Computational Infection BiologyDepartment, led by Thomas Otto, is seeking a highly motivated PhD Student (in data
-
are proud to work together in a truly integrating manner. Our partners, e.g. University of Hamburg, the Max Planck Institute for Meteorology or the German Climate Computing Centre are also hiring. All jobs
-
interdisciplinary research in molecular, structural, and cell biology as well as in physiology, biophysics, epi /genetics, (bio)informatics, and multimodal data analysis. In this project, we will elucidate
-
, (bio)informatics, and multimodal data analysis. The research group led by Dr. Johanna Raidt focuses on the identification of known and novel MMAF- and PCD gene variants using large patient cohorts