168 structures "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" positions at Forschungszentrum Jülich
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
/ Qualification that is highly welcome in industry Further development of your personal strengths, e.g. via a comprehensive further training program; a structured program of continuing education and networking
-
Your Job: In the CrowdING project, you will analyze experimental data from large crowds and develop quantitative measures to describe their spatial structure. To do this, you will use and expand
-
data science courses, soft skill courses and annual retreats) https://www.hds-lee.de/about/ Qualification that is highly welcome in industry Further development of your personal strengths, e.g. via a
-
): https://go.fzj.de/JuDocs SUCCESSFUL START: It is important to us that you quickly settle into the team and are given structured training for your tasks. We also support you from the very beginning and
-
proactively maintaining the relevant websites Your Profile: Completed master`s degree in natural sciences, engineering or information technology, preferably with a PhD Experience with the structures
-
mentoring for building a career in academia or industry Professional development through JuDocS, including training courses, networking, and structured continuing education ( https://www.fz-juelich.de/en
-
Your Job: Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so
-
Your Job: Chromatography modeling, while crucial for modern bipporcess development, still heavily relies on empirical determination of key model parameters. By combining protein structure
-
, structured way of working and motivation to pursue scientific research Our Offer: We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping
-
heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange