62 coding-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" uni jobs at Forschungszentrum Jülich in Germany
Sort by
Refine Your Search
-
thesis will be appropriately remunerated by us In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits We welcome applications from people with
-
found on the BMI website: https://go.fzj.de/bmi.tvoed . The monthly salaries in euros can be found on page 69 and following of the PDF download FIXED-TERM: The position is limited to 31.05.2028 SUPPORT
-
, as well as enjoyment of cooperative collaboration You have a very good command of written and spoken English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ) Our
-
user of supercomputers and sufficient programming skills You have a very good command of written and spoken English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements
-
for personal and professional development with support from experienced PIs. FLEXIBILITY: Flexible working time models, including options close to full-time ( https://go.fzj.de/near-full-time ), allow you to
-
30 days of vacation plus additional days off (e.g. between Christmas and New Year`s) FLEXIBILITY: Flexible working time models, including options close to full-time ( https://go.fzj.de/near-full-time
-
strong Excel skills You are organized, reliable, and proactive You are a good communicator and enjoy teamwork You are comfortable working in English (at least B2 level according to the CEFR: https
-
for the position. In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits We welcome applications from people with diverse backgrounds, e.g. in terms
-
to have a fluent command of written and spoken English with an extensive vocabular (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ), ideally supported by a certificate
-
learning, physics-informed neural networks, graph neural networks, transformers, convolutional defiltering methods, etc.) for the integration in multi-physics simulation codes You will develop code for and