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neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
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, mentoring graduate and undergraduate students. Responsible for reviewing and publishing papers. Brainstorming research ideas and formulating problems. Developing methods and conducting experiments in both
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Five or more years of full-time, continuous, non-academic industry-related experience for all applicants Working knowledge and demonstrated experience with mechanical systems and methods Experience
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of Science, University of Southern Denmark (SDU), Odense. We seek a candidate with strong, documented expertise in computational drug design / computer-aided drug discovery. Preferred competencies include
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multiple institutional research and clinical programs focused on immune repertoire profiling. Building on the foundational TIRTL‑seq (Pogorelyy and Kirk et al., Nature Methods 2025) method, we provide
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 4 hours ago
repeated over several trials (to collect multiple samples) before making a decision. For these applications, statistical methods for hypothesis testing and estimation are needed, with formal guarantees
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14 Apr 2026 Job Information Organisation/Company Instituto Superior Técnico Department Direção de Recursos Humanos Research Field Engineering » Computer engineering Researcher Profile First Stage
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kinesiology. This course also focuses on mixed methods research, participatory action research, and knowledge translation. Estimated TA support: 630 hours Estimated course enrolment: 380 Class schedule: N/A
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Bachelor’s degree in computer engineering, Computer Science, Electronics Engineering or equivalent. Background and familiarity with research in the domain of real-time scheduling in safety-critical cyber
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for age estimation, as well as the development of new, robust methods. To this end: Approaches to be implemented and compared: classical point regression, probabilistic regression, and quantile regression