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the Robotic Intercropping project funded by the Novo Nordisk Foundation. The starting date is expected to be 1 March 2026 or as soon as possible thereafter. The project The Robotic Intercropping project is a 5
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of Physical Chemistry of the Czech Academy of Sciences. The program covers computational chemistry, quantum chemistry, and molecular modeling, focusing on biomacromolecules and complex molecular systems
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-up Decoding of Protein Conformational Landscapes: from Gas-phase to Solution Supervisor: Prof. Scott Habershon, University of Warwick Protein folding is a dynamic, complex process by which proteins
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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | 9 days ago
Job Offer from December 01, 2025 The Department of Molecular Simulations and Design (Prof. Dr. Matthias Stein) at the Max Planck Institute for Dynamics of Complex Technical Systems is inviting
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7 Dec 2025 Job Information Organisation/Company Faculty of Science, Charles University Research Field Chemistry » Biochemistry Researcher Profile First Stage Researcher (R1) Country Czech
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students to join our research groups in physics, astrophysics and space sciences, and Earth and climate sciences. About the department and programme Otago is New Zealand’s oldest university and a leading
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complex dynamics. In parallel, collective decision-making mechanisms (e.g., opinion dynamics) will be leveraged at the high level to coordinate the desired behaviours of multi-agent systems in response
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This PhD is part of the The Fusion Engineering Centre for Doctoral Training (CDT), at the University of Nottingham and has industrial collaboration, being co-supervised by a member of staff at
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explore the phase behaviour of potassium and unlock new understanding of alkali metals' unique physical properties. At high pressures and temperatures, these metals reveal complex phase transitions and
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(CHF), tailored to complex geometries typical of fusion reactor cooling systems. Compile a comprehensive dataset of boiling parameters to support machine learning-based analysis of two-phase flow