311 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"ISCTE-IUL" positions in Denmark
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, that can be documented by a publication record in relevant venues. Solid understanding of state-of-the-art embedded machine learning techniques. Experience in system-level programming, developing prototype
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Job Description The Centre for Machine Learning within the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark
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on Nanoparticles You will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network
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, Bash). Experience working in a Unix/Linux environment, including setting up and managing High Performance Computing (HPC) clusters. Familiarity with metagenomic data analysis and machine learning
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labelled images. Close collaboration with the rest of our interdisciplinary team at DTU Construct and Vistacon, particularly the other postdoc position focusing on image analysis using deep learning. Please
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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workflows Experience in quantitative data analysis and computational approaches; familiarity with machine learning or advanced statistal methods is advantageous Preferably experience with micro-CT imaging
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(supervised by Assoc. Prof. Ivana Konvalinka) and machine learning researchers (co-supervised by Prof. Lars Kai Hansen), you will be responsible for designing and running interactive multi-person (hyperscanning
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expected to learn new laboratory techniques and become able to use them autonomously. Furthermore, he/she will attend weekly seminars and laboratory meetings. The working hours are 37 hours per week. For
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electrical energy storage systems; energy management systems. Experience with data processing, statistical analysis and machine learning techniques is an advantage. Knowledge with Mathworks suite, C/C++ and