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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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of doctoral and post-doctoral researchers from diverse backgrounds (e.g., economics, computer science, information systems, engineering, etc.), united in pursuit of sustainable solutions that positively impact
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should possess an M.Sc./M.Eng. degree in one of the following fields: Electrical and Electronics Engineering, Telecommunications Engineering or Computer Science/Engineering. Experience: The candidate
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computer science and information system engineering while collaborating in a publicly funded research project with our industry partner Encevo. In this project, the aim is to analyze and build a robust anonymization
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platform of analogue hardware accelerators, so-called Ising machines, that efficiently speed up these computationally difficult tasks in a way unlike any current digital computer. These Ising machines are a
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/electrical engineering, or a related field.Strong interest in automation and laboratory robotics.Basic experience in wet-lab microbiology techniques.Willingness to learn programming and computational analysis
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the disciplines of law, ethics or computer science with the common goal of researching methods and tools to make Integrated Assessments of risks and impacts for current Data intensive and AI technologies. Research
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/electrical engineering, or a related field. Strong interest in automation and laboratory robotics. Basic experience in wet-lab microbiology techniques. Willingness to learn programming and computational
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degree or equivalent in Robotics / Aerospace / Mechatronics / Mechanical or Electrical Engineering, or related fields in Engineering or Computer Engineering Programming Skills: Design tools for Mechanical
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Project description We are a highly motivated international team of researchers at the Molecular Neurogenomics group (Jordanova Lab) and the Computational Neurobiology group (Malysheva Lab