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Management Lab (KDMLAB) of the Department of Computer and Information Science at Linköping University. The department is one of the largest computer science departments in northern Europe, with research
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, or robot perception. Strong programming skills in Python and/or C++, with experience in robotic software frameworks (e.g., ROS/ROS2). Experience with machine learning or computer vision methods, preferably
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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, we offer a dynamic, collaborative ecosystem. The AI and Machine Learning in the Natural Sciences (AIMLeNS) lab is a tight-knit team of computer scientists, chemists, physicists, and mathematicians
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-Geometric Foundations of Deep Learning or Computer Vision KTH Royal Institute of Technology, School of Engineering Sciences Job description The Department of Mathematics at KTH welcomes applications for a
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(AIMLeNS) lab is a tight-knit team of computer scientists, chemists, physicists, and mathematicians working collaboratively. Our focus is on developing practical methods that blend traditional disciplines
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other relevant qualifications A high level of computer proficiency, particularly in advanced imaging and image analysis, FACS, in vitro and/or in vivo assays Very high motivation, ambition and enthusiasm
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» Biochemistry Chemistry » Other Computer science » Informatics Computer science » Computer systems Computer science » Digital systems Medical sciences » Medicine Medical sciences » Other Researcher Profile First
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complex systems. Development and application of theoretical tools that combine experimental data and atomistic computer simulations to provide a comprehensive picture that is difficult to achieve through
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that support the unit for area protection and marine spatial planning, as well as operations at SLU Aqua. Your profile You have documented expertise in marine ecology and computer vision and machine learning