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on inorganic, organic, polymer and materials synthesis, as well as advanced structural analysis and characterization, with applications spanning nanomaterials, energy storage, and catalysis. Work duties The
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. With cutting-edge research, top-tier education, and extensive collaborations, we are a key force in the field. Our core competencies include in vitro ADME models, advanced in vivo methods, computational
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Biomedical Science working on molecular modeling of proteins and lipids to investigate biomolecular condensates at cell membranes. Project This PhD project investigates how proteins interact and organize
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collaborations, we are a key force in the field. Our core competencies include in vitro ADME models, advanced in vivo methods, computational modeling and simulation, as well as patient-focused and policy-related
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focuses on developing a fundamental understanding and numerical models for multiphase flows, which are crucial for various industrial processes. The successful candidate will develop advanced physics-based
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qualifications You have graduated at Master’s level in Biology/Medical Biotechnology/Genetics or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses Biology/Genetics
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level in computer science or completed courses with a minimum of 240 credits, at least 60 of which must be advanced courses in computer science, mathematics, AI, machine learning or similar. Alternatively
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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and career advancement across the globe. DDLS industrial PhD position We are announcing the position of Data-driven life science (DDLS) PhD student in data driven cell and molecular biology. This is an
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of computation, and thus continuous aspects, into rule-based models of graph transformation in order to combine the individual strengths of both paradigms. Rule-based models are transparent and explainable