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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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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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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
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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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-learning algorithms Versatile data-science knowledge, including image and DNA sequences processing Programming skills in Python or other modern programming languages supporting AI and bioinformatics
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of software interfaces and data models Selection and configuration of algorithms for annotating and organizing research data and software using state-of-the-art AI technologies Ensuring the sustainability and
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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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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data Development of algorithms for infection and evaluation of infection hotspots in the plant population Coordination of the scientific interface to the project partners with regard to entomological
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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