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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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enable the development of fault-tolerant quantum computing hardware and quantum algorithms that solve life-science-relevant chemical and biological problems. Within NQCP we are currently pursuing several
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to have experience with: Phase equilibrium calculation algorithms and their integration into CO2 capture simulation Thermodynamic modeling of phase equilibrium and thermophysical properties related to CO2
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the project. Disseminate research findings through publications and presentations at international conferences. Expectations of qualifications: PhD in Ecology, Evolutionary Biology, or a related discipline
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solutions and policy impacts. You will design and implement machine-learning algorithms that interact with your simulation framework for scenario discovery, building surrogate models of simulation outputs
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the Department of Immunology and Microbiology, Faculty of Health and Medical Sciences. This role involves developing and applying advanced bioinformatics algorithms to investigate the functional diversity
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techniques for integrating such solutions into modern SDV middleware. Responsibilities: Conduct research in runtime analysis and reconfiguration of in-vehicle TSN networks. Develop algorithms and prototypes
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will develop in the position; it is expected that you have previous experience on each of them: Develop and implement CFD models to simulate the behavior of PRO systems. Apply ML algorithms to optimise
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learning representations and improve their interactivity. Make AI explanations more understandable Machine learning algorithms often appear as complex black boxes and much research goes into visualizing
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key responsibilities will include: Designing and implementing advanced LabVIEW and C++ based control software for our HS-DAFM platform Developing specialized signal processing algorithms and circuits