14 parallel-processing-bioinformatics-"https:" positions at Aalborg Universitet in Denmark
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
of Computer Science, Section for Copenhagen, Natural Language Processing The Department of Computer Science at The Technical Faculty of IT and Design invites applications for four postdoc positions in the area of
-
of Computer Science, Section for Copenhagen, Natural Language Processing The Department of Computer Science at The Technical Faculty of IT and Design invites applications for four fully funded PhD stipends in the area
-
membrane processes for critical metal recovery from spent lithium-ion batteries, is open for appointment from 1 March 2026 or soon hereafter. The duration of the position is 24 months. Your work tasks The
-
conditions during flame-wall interaction (e.g., wall distance and injection pressure) on combustion, heat transfer and emission characteristics in the CVCC. The position is offered in relation to the research
-
advance, CLASSIQUE focuses on a critical challenge: how to evolve classical communication networks to support both traditional data and the unique requirements of quantum information systems (https
-
for reliable operation in extreme environments. Our research strengthens robotic autonomy and resilience, enabling systems to operate safely and with minimal supervision in challenging settings. How to apply
-
, MATLAB/Simulink, and simulation environments like Gazebo or Unity. Experience developing software for real-time sensor processing or robotic systems is expected. Soft skills: Strong communication skills in
-
research works can be found at , https://scholar.google.com/citations?user=6Y-m8k4AAAAJ&hl=en , and at https://dblp.org/pid/67/2933.html . Within the area, the position comes with many freedoms in terms
-
Augmentation and Collaboration ; Natural Language Processing ; Physical and Embodied Interaction ; Probabilistic and Symbolic AI ; Quantum Systems Analysis and Synthesis ; Systems Design & Development Practices
-
. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models for long-term forecasting. Collaborate closely with