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the development of computational methods for genetics, high-throughput omics data and causal discovery. Our interdisciplinary and international team is jointly located at DKFZ and EMBL Heidelberg, and
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Your Job: Developing and implementing QC algorithms (QAA, QAOA, QSVM), quantum AI algorithms, use case adapted algorithms to test and benchmark latest technology focusing on gate-based QC Advancing
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equivalent degree in agriculture, biology or any related field. You are an early career researcher not more than one year after your PhD and you have a proven track record and extensive experience with genetic
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populations. This project will explore the genetic and evolutionary mechanisms shaping adaptation through a combination of genomic, computational, laboratory, and field-based approaches. Research focus
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- conducting processors with respect to practical short-depth (NISQ) quantum algorithms Cooperate and actively work with experimental partners developing quantum processors using these technological platforms
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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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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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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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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with several research groups working in related fields, particularly in algebraic geometry and algorithmic algebra. For more information about the TUM Department of Mathematics, please visit our website