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students who would like to write their final thesis in the field of machine learning / computer vision. The primary goal of this master’s thesis is to develop an algorithm that can accurately and efficiently
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into account various parameters. To this end, a concept is developed, various mathematical models and Machine Learning algorithms are selected and then tested and evaluated within a company environment. What you
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for autonomous systems. Our focus lies on safety-critical applications in the fields of automation, mobility and health. We develop reliable software technologies with a benefit for humans. For example, we conduct
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developing a machine learning (ML) algorithm for the automated analysis of the above-mentioned mass spectra. Desirable: - knowledge in the field of Planetary Sciences - very good written and spoken English (C1
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algorithms and Quantum Imaginary Time Evolution. What you bring to the table Experience with Python programming Experience in programming quantum algorithms with Qiskit, Pennylane, or similar Prior knowledge
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researched and developed that will be used in current and future key topics. Become a part of our team and join us on our journey of research and innovation! What you will do Test new deep learning
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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is to investigate which antigen specificities are enriched in cell subpopulations, depending on the underlying neurological disease. The project will use high-throughput data to develop and apply
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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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to their spin properties. What you will do You will program the connection of quantum experiments to an existing HPC system for the external execution of quantum algorithms. You will develop an efficient user