41 structural-engineering-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" scholarships at Forschungszentrum Jülich
Sort by
Refine Your Search
-
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
-
): 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
-
and individually, for example through training opportunities and the structured JuDocS program for doctoral candidates: https://www.fz-juelich.de/en/judocs In addition to exciting tasks and a
-
Infrastructure? No Offer Description Area of research: PHD Thesis Job description: Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use
-
to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so-called lean alloys, and material systems with a high tolerance to impurities from
-
Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem
-
. or equivalent) in applied mathematics or in computational engineering science, computer science, simulation science with a strong background in applied mathematics Excellent programming skills (Python, C/C
-
of experiments, measurement methods and measurement technology, including test facilities with regard to the research and project objectives Collaboration in the analysis of operated SOC stacks through disassembly
-
work Your Profile: Completed university degree (Master`s) in a subject with a strong focus on chemical engineering, e.g. process engineering, mechanical engineering, technical chemistry, relevant
-
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