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Field
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propagation modeling and data analysis, machine learning, algorithm design and validation Experience in performing lab measurements and collecting datasets for validation of developed algorithms Good
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lasers, optics, and optical emission spectroscopy. Experience designing custom experimental hardware, vacuum systems, and plasma diagnostics. Experience applying machine learning methods for
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join a new initiative focused on designing and developing a machine perfusion system to improve organ preservation. The candidate will work in a dynamic, multidisciplinary environment alongside PhD
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join a new initiative focused on designing and developing a machine perfusion system to improve organ preservation. The candidate will work in a dynamic, multidisciplinary environment alongside PhD
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quantum algorithms and their optimization, adiabatic quantum computation, and quantum machine learning. Theoretical and applied research into the capabilities of quantum computing and quantum
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computation, and quantum machine learning. Theoretical and applied research into the capabilities of quantum computing and quantum advantage is a prospective topic across four application domains
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/software co-design and operating/runtime systems. Typical application domains are e.g. signal-/image processing and machine learning. Tasks: modeling and simulation of near-memory computing
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optimization, adiabatic quantum computation, and quantum machine learning. Theoretical and applied research into the capabilities of quantum computing and quantum advantage is a key focus across four application
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researchers across the ACT to identify emerging theoretical challenges motivated by astrodynamics, optimisation, control, scientific machine learning, mission design and autonomous systems, translating
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materials characterization Strong background in computational materials science and machine learning applications Proficiency in programming and data analysis languages (LabVIEW, Python, MatLab, OriginPro