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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling
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reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling. Central to its
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
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: Dynamical Systems Control Theory Formal Methods Reachability Analysis Computational Geometry Context The applicant will be directly advised by Prof. Matthias Althoff (https://www.ce.cit.tum.de/cps/members
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biology, strong communication skills, and excellent written and oral command of English are required. The application should include a curriculum vitae, a motivation letter, degree certificates, the
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to the deadlines Ability to communicate eloquently with senior stakeholders Ability to write reports and research articles Experience with guiding staff on producing deliverables Excellent command of English
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-Boltzmann methods and their implementation, as well as high-performance computing experience. • A solid command of English in speaking and writing. Knowledge of German is not required. We offer: An exciting
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journals Excellent command of written and spoken English Proficiency with structured coastal ocean models (e.g., ROMS, GETM, CROCO) Familiarity with the hydrodynamics specific to the Baltic Sea region (also
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the English language is used in all scientific discourse, and, despite its cozy size, Heidelberg is a famously international city. Our laboratory has developed a powerful, highly controlled cancer induction system using
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criteria: Proven track record of publishing high-quality scientific articles in peer-reviewed journals. Excellent command of written and spoken English. Proficiency with advanced statistical methods