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resistance, via machine learning approaches. This doctoral project also foresees three secondments, each for the duration of three months, during which you will have the opportunity to visit partner
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organization-specific. This poses a critical challenge for applying machine learning: there are typically only a few examples of each specific fraud pattern to learn from. Classic machine learning methods
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environment The Neuroengineering Lab offers an interdisciplinary environment with expertise in mathematical modelling and analysis, CMOS design, machine learning, computational and experimental neuroscience, as
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engineering or mathematical engineering Good understanding of statistics and machine/deep learning algorithms Interest in Biomedical data science Excellent programming skills in Python Proficient English, both
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that combines machine learning and knowledge-based inference. In real-world applications, it is often paramount to exploit expert knowledge for the task at hand. However, this poses significant challenges with
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thesis in Artificial Intelligence and Cyber-Physical Systems, with a strong emphasis on Explainable AI. This PhD will investigate how CPS can clearly explain their proactivity learning, the reasoning
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assets Practical experience in fuzzing or cybersecurity testing. Familiarity with machine learning concepts or AI platforms. Curiosity, creativity, and the drive to explore new research ideas. We offer
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skills and a keen interest in application-oriented academic research in applied machine learning. The selected candidate will conduct a research project on data-driven fraud detection, with the objective
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power consumption trends or including the energy penalty of machine learning solutions themselves. And the energy efficiency at the transceiver hardware will be put in a broader perspective of
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for the position are : Obtained a first class Master in a relevant field, e.g. computer science, biomedical engineering or mathematical engineering Good understanding of statistics and machine/deep learning