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neuromodulation technique capable of targeting deep brain structures in a non- or minimally invasive manner. You will play a central role in the preclinical research activities of the project. YOUR JOB Perform
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are looking for a m/f/x Doctoral fellow YOUR JOB You conduct doctoral research in the area of Augmenting Learning Environments Using Generative AI and Neuroadaptive Systems, with the aim to obtain a PhD after
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modeling into modern causal inference by combining its strengths with innovations in debiased machine learning, as well as to improve both the statistical efficiency and robustness of debiased machine
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will research strategies on including physics and enhance active learning as well as aim at reducing catastrophic forgetting. You will research how to uncover uncertainties that are hidden to sensors and
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(preferably) in Civil Engineering-Architecture, Civil Engineering or equivalent degree. You enjoy performing research. You are independent, self-motivated and eager to learn. Experience and/or a master thesis
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affinity for working in a laboratory environment. You are eager to learn and motivated to further develop your knowledge and skills. You work with precision and are capable of collecting, documenting, and
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interdisciplinary research team, focusing on human-robot interaction, unconventional robotics, cognitive systems and machine learning. You will have access to the full facilities of IDLab, including high-performance
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at national and international conferences. You may be asked to teach exercise sessions (in English) for a course taught by the department staff, or to help in the supervision of master’s theses. Your ultimate
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-of-the-art in machine learning, (probabilistic) modelling, system identification and numerical optimization. How to apply Send your CV, containing one or more references, a copy of your diploma (if already in
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. Digital Extraction from Historical Taxonomic Literature Application of OCR and machine learning algorithms to digitize printed and handwritten documents; Linking specimen mentions in literature to digital