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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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employees as well as its students. YOUR TASKS At least 50% of your assignment will be spent on academic research in preparation of a doctoral dissertation in the disciplines robotics and machine learning
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and practical experience with modeling and machine learning software NONMEM, Monolix, Simcyp, PK-Sim, Gastroplus, R and/or Python is a plus You have excellent teaching and communication skills Any
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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
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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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initiated research Advantages strengthening the candidate’s profile, but not explicitly required: Knowledge of machine learning and system optimisation; Python or MATLAB programming. Having published as (co
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experience with quantitative research methods, including psycho-physiological and behavioral measures and advanced statistical (and/or machine learning) methods. You have a high level of proficiency in English
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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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a strong interest in and/or experience with quantitative research methods, including psycho-physiological and behavioral measures and advanced statistical (and/or machine learning) methods. You have a
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be maintaining a pool of diverse, complementary models that can be adapted to new problems through continuous learning. We will develop this methodology to address several applications within the life