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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for a PhD stipend in the field of machine learning and earth observation within the general study
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conduct high-quality and impactful research. A key focus of the position is the study of the role of machine learning and artificial intelligence in healthcare and public policy, including
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employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning
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to: Gas sensor selection and characterization Embedded system integration Mechanical design (CAD) and 3D printing Data collection and analysis Basic machine learning for sensor data interpretation Start
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Molecular Biology, University of Southern Denmark, Odense, Denmark The position is for 3 years and is available from February 1, 2026. Role and Responsibilities Use protein design concepts and deep-learning
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handling robot, and running our Illumina NovaSeq instrument and other instrumentsrelated to NGS e.g., qPCR machine and Fragment Analyzer. In addition, you will be involved in supporting the research projects
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research competence, critical thinking, and disciplinary expertise. Strong expertise in analytical AI and mandatory hands-on experience coding with machine learning frameworks like TensorFlow, PyTorch
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Assistant Professor (tenure-track) and Associate Professor (tenured) Positions in Computer Scienc...
, Topology and Algebra Learning Experience Design Our department offers an inclusive and international working environment with state-of-the-art facilities and strong traditions for collaboration across
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, Machine Learning for photonic systems, as well as Photonics in general. Your track record proves your position as an internationally recognized researcher in your field and confirms your ability to lead
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crystal handling, SPR machines, and two libraries of fragments (small molecules <300 Da) tailored for SPR and X-ray crystallography. This setup enables a full workflow for fragment-based drug discovery