10 molecular-modeling-or-molecular-dynamic-simulation PhD positions at AALTO UNIVERSITY
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of tomorrow and creating novel solutions to major global challenges. Our community is made up of 13 000 students, 400 professors and close to 4 500 other faculty and staff working on our dynamic campus in Espoo
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to 4 500 other faculty and staff working on our dynamic campus in Espoo, Greater Helsinki, Finland. Diversity is part of who we are, and we actively work to ensure our community’s diversity and
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other faculty and staff working on our dynamic campus in Espoo, Greater Helsinki, Finland. Diversity is part of who we are, and we actively work to ensure our community’s diversity and inclusiveness
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dynamic campus in Espoo, Greater Helsinki, Finland. Diversity is part of who we are, and we actively work to ensure our community’s diversity and inclusiveness. This is why we warmly encourage qualified
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developments in learning Operator Theoretic representations of dynamical systems that focus on model interpretability, scalability to high dimensions, and data efficiency. The exact direction of the research is
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stability theory, modeling & identification, optimal control, certifiably safe & robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven
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of tomorrow and creating novel solutions to major global challenges. Our community is made up of 120 nationalities, 14 000 students, 400 professors and close to 5000 faculty and staff working on our dynamic
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systems (e.g., GC) Experience in synthesis and characterization of heterogeneous catalysts Hands-on experience with (pressurized) chemical reactors Experience with modelling and simulation software (e.g
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-on experience with (pressurized) chemical reactors Experience with modelling and simulation software (e.g. Aspen) Good organization and data management skills We expect the candidates to be able to start
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, fairness). Provenance and integrity of machine learning pipelines. Generative content authenticity. Cyber-physical machine learning systems. Scalability of properties from small to large models. In