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of the Center for integrative neuroplasticity (CINPLA) and in the INTED center. This PhD project will focus on reinforcement learning methods for generating complex structures with two possible application areas
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the small-scale effects on the material behavior of PNSEs, only molecular simulations can provide a systematic method to understand the relationship between the structure and function of the complex systems
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them efficiently is becoming increasingly complex. Variations in terrain, road conditions, and traffic patterns increases the complexity further to effectively plan their movement and deployment
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-project As mobile energy storage systems become more widely adopted, managing them efficiently is becoming increasingly complex. Variations in terrain, road conditions, and traffic patterns increases
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with 20 % teaching and administrative duties. More about the project and the PROMENTA Research Center The purpose of the project is to increase our knowledge of the complex interplay between genes and
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to address complex data structures Personal skills Creativity and ability to work independently Self-motivated, eager to learn and ready to tackle and deal with challenges Able to collaborate with others in a
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thinking Able to work independently and collaboratively Structured and able to manage complex projects Strong in analytical thinking and problem solving Communicative and able to work across disciplines
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machine learning and AI techniques to improve prediction of contaminant transport, sediment dynamics, and ecosystem exposure in complex fjord environments. The research will benefit from extensive
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working both independently and as part of a team, take ownership of your research questions, and be comfortable communicating complex ideas clearly across disciplines. A collaborative spirit, a deep sense
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understanding the dynamical and structural effects of various peptides on both simplified and complex membrane systems. The project will entail a combination of computational and experimental work, both directly