64 coding-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"FIIB-HUIS-HUHEN" positions at Nature Careers
Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
) 13 TV-L FU reference code: ONEMuc-FluBind2 The collaborative project ONEMUC – Respiratory Mucus as a One Health Interface investigates how the composition and structure of mucosal barriers (mucus
-
) 13 TV-L FU reference code: ONEMuc_FluBind The collaborative project ONEMUC – Respiratory Mucus as a One Health Interface investigates how the composition and structure of mucosal barriers (mucus
-
Systems with a focus on Enzyme engineering (Reference code 37) Working time: 40 hours per week Term of the employment relationship: 1st of October 2026, limited to a period of 6 years Allocation in
-
TV-L FU reference code: 637 Who We Are The position is assigned to the workgroup Kuch, which has experience investigating ultrathin magnetic films, surfaces, nanostructures, and adsorbed molecules
-
, CRISPR-Cas systems, microRNAs, non-coding RNA, RNA biology of infections, and RNA chemistry. Applicants can choose a mentor who best matches their interests and background (more information under “Panel
-
strong background in optimization and machine learning. Good coding skills in Python, PyTorch are welcomed. Application Applications should contain a CV, a motivation letter, the grade records of the last
-
The Department of Biotechnology and Food Science, Institute of Food Technology is currently seeking a Postdoctoral Research Associate (Reference code 14) Extent of employment: 40 hours per week
-
and coordination of interdisciplinary teams Excellent record in open-source software development and experience in collaborative software development practices (e.g., version control, code review
-
policy-as-code for real-time safety checks Parameter-efficient fine-tuning of LLMs for age-appropriate, pedagogically meaningful dialogue High-performance computing pipelines on Luxembourg's MeluXina
-
., pyTorch, TensorFlow) Proven publication record in relevant fields Familiarity with high-performance computing environments Familiarity with using and adapting open-source AI models and code repositories