159 algorithm-"the"-"Embry-Riddle-Aeronautical-University"-"The-Netherlands-Cancer-Institute" positions in Germany
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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with the latest sensors (camera and LiDAR sensors), is available for the work. What you will do – development of algorithms for 3D multi-object tracking based on heterogeneous sensor data fusion
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programming of algorithms. The use of programming languages such as Python, R, SQL, and C++ will be a daily part of the project, and proficiency in these languages is required. However, additional datasets will
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? You have some experience in research and are familiar with maritime navigation or optimization algorithms? Great! You also have solid Python skills and enjoy working analytically and documenting your
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algorithmic algebra. For more information about the TUM Department of Mathematics, please visit our website: https://www.math.cit.tum.de/en/math/home/. The position is a full-time position (100%), initially
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algorithms in extremely complex and enormously large spaces motivated by physics and chemistry Developing interpretable AI for scientific discovery in physics (example here ) Formal mathematics (using Lean’s
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for technologies Conscientiousness in implementing, testing, and documenting algorithms Curiosity about a deeper understanding of deep Learning architectures Experience in academic writing is an advantage What you
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, and documenting algorithms High degree of proficiency in spoken and written English What you can expect Fascinating challenges in a scientific and entrepreneurial setting Attractive salary Modern and
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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implementation, practical application, theoretical analysis and evaluation of AI algorithms Use of XAI tools to explain machine learning models Implementation of deep learning Improvement of models, e.g. in terms