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Universitätsklinikum Heidelberg – UK Mannheim GmbH | Mannheim, Baden W rttemberg | Germany | 6 days ago
optimize bioinformatic tools, databases and workflows Collaborate with researchers to design experiments and analyze results Develop custom scripts and algorithms for data processing and visualization Your
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, but this option can be time consuming and unsafe when testing complex algorithms (navigation, grasping . . . ). Using a simulator can speed up development, is safe, but breaks the natural interaction
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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of the thesis is to develop an integrated framework for evaluating ecosystem responses to emerging blue bioeconomy activities under different management scenarios. To achieve this, the study combines in situ
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the essentials of linear algebra, calculus, probability, statistics, algorithms, and data structures. The goal is to provide a foundational understanding of machine learning and deep learning methods
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Livingston Observatory, Caltech, and MIT. As LHO Scientist, you will have the opportunity to apply your skills to the design, development, implementation, and commissioning of technologies that increase
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software Analyze software for new, moderately complex systems and algorithms Perform data analysis, test and debug software Develop, implement and execute plans and tests Design and apply basic data sources
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For the doctoral programme in question, the following are considered as other qualifications: Experience in scientific programming and algorithm development. Knowledge/experience in wireless communication systems
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Student (f/m/d) in Quantum Algorithms for Droplet and Bubble Oscillation Dynamics Modelling. Your tasks Development and implementation of numerical and algorithmic methods for the simulation of fluid
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: The objective of this task is to develop a decision-support model to assist in the selection of diagnostic and prognostic algorithms by jointly optimizing energy and computational costs. Two goals are pursued: (i