Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
, systems integration). Perception and machine learning for robotics. Software design and development (C, C++, Python), including real-time and embedded systems. UAS operations (experiments, piloting
-
multibody dynamic simulation software using modern approaches (e.g., Kane’s method). Implementation of control algorithms and robotics for controlling hexapod-like loading systems. Excellent English
-
in interdisciplinary teams including clinicians, software developers, and health economists Excellent communication skills in both Danish and English (written and oral) We offer A stimulating
-
patterns of stress resilience 4. Identify candidate genes associated with key agronomic traits related to resilience 5. Contribute to software and web development Qualifications PhD degree in Bioinformatics
-
-reviewed journals; collaboration in interdisciplinary and international research environments. Experience with software such as R, Python, SPSS, Stata, Sawtooth, Qualtrics or similar tools will be
-
. Demonstrated ability to independently develop, modify and troubleshoot bioinformatic workflows, including diagnosing computational and analytical problems when established pipelines or software are
-
correctness. Valued: Experience with high performance computing (HPC). Prior exposure to mass spectrometry, omics, audio, or other high-dimensional structured data. Interest in open-source software
-
quantitative profile. The ideal candidate will have: strong and documented quantitative analysis skills; advanced proficiency in Stata, R, or comparable statistical software; the ability to specify
-
Scandinavia Collaborate with beamline scientists, software developers, and technical staff to improve instrumentation and user workflows Collect, process, and interpret SCXRD data for academic and
-
Postdoctoral position for the project Human AI Collaboration: Imaginaries, Interventions, Interfaces
literature, critical data studies, and software studies, and it analyses how GenAI is interfaced (SP1), how GenAI creates new relations to archives (SP2), experiments with prototyping alternative interfaces