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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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optimization of single-phase LCL filter inductors taking into account dynamic hysteresis models for different magnetic core materials. Supervisor: Prof. Paavo Rasilo (Electromechanics) Secondments: Université
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.0c02887 ). We are looking for a motivated student with keen interest towards experimental research and applications of trace gas analysis. An optimal candidate should have prior experience with optical
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skills and experience in written, spoken, and collaborative work in English. What we offer: A career path optimized for transition to industry. You will be affiliated with the dynamic Aalto Scientific
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leverage the theory of estimating functions to create optimal inference algorithms for the proposed loss functions based on its underlying Riemannian geometry, as well as for those previously introduced in
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in real-time experimental settings and optimize them for privacy-preserving healthcare monitoring. You will collaborate with healthcare professionals to validate solutions in use cases such as
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chemical activation method, and develop environmentally friendly electrolytes. Work will focus on: Preparation, characterization, and optimization of porous carbon electrode ink for supercapacitor
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, simulation environments and optimal control are appreciated. English language requirements: Proficiency in written/spoken English is mandatory. In certain cases, we may ask for a language certificate. We offer
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technologies to facilitate efficient optimization of the enzymes and products. The applications of this research span enzyme technologies to functional biomaterials. Scientific environment The Department
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) interest in high-performance computing and algorithm optimization (e.g. GPU computing) ability to work both independently and in collaboration within a multidisciplinary research environment initiative and