72 computer-science-intern "https:" "Simons Foundation" Postdoctoral positions at Aarhus University in Denmark
Sort by
Refine Your Search
-
The Section for Electrical Energy Technology at the Department of Electrical and Computer Engineering (ECE), Aarhus University, is in a phase of rapid growth in both education and research
-
Targeted mutagenesis / protein engineering and screening of muteins Computational biology on an HPC cluster Teaching experience with bachelor and master students Candidates should have the following personal
-
(2014). https://doi.org/10.1126/science.1253920 [2] An RNA origami robot that traps and releases a fluorescent aptamer. Science Advances (2024). https://doi.org/10.1126/sciadv.adk1250 Your qualifications
-
(2014). https://doi.org/10.1126/science.1253920 [2] An RNA origami robot that traps and releases a fluorescent aptamer. Science Advances (2024). https://doi.org/10.1126/sciadv.adk1250 Your qualifications
-
Zheng. Your competences You have academic qualifications at PhD level, for example within computational biology, bioinformatics, spatial omics, or related areas. Experience with computational imaging
-
next‑generation insect camera traps with on‑device (edge) computing for real‑time detection and classification Collaborating in an interdisciplinary team spanning ecology, computer science, engineering
-
We are seeking applicants for postdoc positions in ‘Mammalian Nuclear RNA Production and Turnover Systems’ to join us at the Department of Molecular Biology and Genetics in the research team
-
. Researchers in the section teach the BSc and MSc programmes in animal and veterinary science and supervise PhD students and conduct research-based public sector consultancy for national and international
-
of Molecular Biology and Genetics at Aarhus University seeking to understand RNAs role in the onset of Darwinian evolution. The lab takes inspiration from simple natural replicons for engineering RNA systems
-
at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum