Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
the invasive comb jelly Mnemiopsis leidyi, one of the very few animals known to produce coelenterazine, as model organism in our laboratories at University of Copenhagen. Apart from studying bioluminescence in
-
At AAU Energy, a position as Postdoc in AI and Deep Learning for Radar-Based Non-Destructive Testing is open for appointment from 01.10.2026 or as soon as possible hereafter. The position is
-
invites applications for a one-year postdoc position, focusing on cellular neurobiology involved in neuropeptide signaling. The candidate is expected to use cutting-edge cell biology techniques, proteomics
-
availability, for instance due to load balancing requirements. Model parameters will be continuously updated using measured and estimated data from the physical pilot plant, providing a foundation for
-
microscopy and imaging approaches Hands-on experience with genome editing technologies (CRISPR-based techniques) Hands-on experience with cellular differentiation systems, stem cell biology and cellular models
-
quality, including publication. Establishing pre-clinical models of prostate cancer including in vivo (in conjunction with the Thomsen group), cell-line co-culture and organoid models. Sectioning tissue
-
studies using kidney cell culture models to investigate receptor–ligand interactions and endocytic pathways. Perform and optimize molecular, biochemical, and histological techniques, including
-
molecular and cell biology, transcriptomics, epigenomics and metabolomics, bioinformatics, immune fluorescence imaging, electrophysiology, and animal models of immunity in health and disease. The lab's
-
with sustainability assessment methods, including life cycle assessment and related modelling approaches, and you have applied these in cases and analysis related to biobased products and the bioeconomy
-
. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust