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Field
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(CV, list of publications, teaching concept (1 page) and research concept (max. 3 pages), list of third-party funding, copies of certificates and degrees) online at https://berufungen.fau.de by October
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/job-vacancies#Professorships). Applications (CV including a list of publications, list of courses taught, experience obtaining external funding, as well as copies of certificates and diplomas) are to be
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Research Assistant (m/f/d) in the field of Theoretical Ecology and Evolution or Computational Biolog
of written and spoken English Additional Qualifications: Sound knowledge of concepts and theories in ecology and evolutionary biology Practical experience in working with experimental scientists Ability
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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | about 2 months ago
Experience in HPC computation (application and algorithm/code development) Willingness to closely collaborate with experimentalists and theoretician. Joint research approach of all ERC synergy team members
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Pernice (wolfram.pernice@kip.uni-heidelberg.de ) or Kim Boonekamp (k.boonekamp@dkfz-heidelberg.de ). Please include a motivation letter, curriculum vitae, a list of publications, a list of current research
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algorithms for application parallelization, simulators and virtual platforms for application- and architecture exploration, hardware/software co-design and operating/runtime systems. Typical application
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3D software using machine learning tools such as neural networks. You will perform quantitative analyses on existing large amounts of tomographic imaging data and improve the evaluation algorithms in
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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for autonomous drone systems Multi-agent cooperation using Reinforcement Learning and control barrier functions Mission planning for aerospace systems using evolutionary algorithms and Reinforcement Learning
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energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity