173 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S"-"U.S" positions in Netherlands
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supportive environment where team members learn from one another, encourage collaboration, and strive to advance their expertise in law and digitalization. The candidate is expected to actively build and
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to stay or move London School of Economics, London, UK Beth Lloyd (Leiden University) Causal contributions of (zero) prediction-error signals to learning and decision-making using transcranial ultrasound
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colleague who: holds a Master’s degree in mathematics or computer science; has a solid foundation in category theory; is familiar with dependent type theory; is enthusiastic about learning advanced category
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Social Science, or related field; has strong affinity with the study of families and economic inequality; has experience with both quantitative and qualitative research (or is motivated to learn both types
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specification and evaluation, is required. Experience with the design, development and verification of TT&C and PDT subsystems for space applications is required. Very good knowledge of modern computer systems
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the field of EMC in general, with demonstrated expertise in analysis and proven practical experience in testing. Expertise with analogue electronics design, computer-aided design or electromagnetic simulation
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experimental and theoretical (CFD) research. You will establish and lead a research team, working closely with your colleagues. You will actively acquire and carry out research projects with academic and
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are educated in a stimulating learning environment. Upon graduation, they have acquired the knowledge, insight and skills to make important and inspiring contributions to an increasingly international society in
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based on scientific research at the intersection of the Humanities and Social Sciences. Our ambitious Dutch and international students are educated in a stimulating learning environment. Upon graduation
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quantitative research and data analysis, with openness and desire to learn and apply diverse data sources (e.g., lab and survey experiments, surveys, administrative microdata) and methodologies (e.g., multilevel