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in nurturing and expanding our ecosystem of corporate members and are expected to hit sales targets. Your role is crucial in guiding corporate clients towards their objectives, showcasing our offerings
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/Seurat, count models, batch correction, differential analyses). Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles. Bioinformatics
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/Seurat, count models, batch correction, differential analyses). Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles. Bioinformatics
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bottom-up approach to robotics and develop soft materials and devices that would enable unusual form and unconventional functions for broader robotic applications. Job description Track 1: Fiber-based soft
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Must haves for postdocs (all tracks): PhD degree in Materials Science, Mechanical Engineering, Electrical Engineering or related field from a top university Strong experimental background with
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cell-fate tracking tracking and studying various stages of the metastatic cascade, we set out to follow tumour cell-niche interactions to reveal how distant sites shape cancer progression-and where we
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, multi-objective optimisation (e.g., genetic algorithms), gait analysis/biomechanics. Proven track record in deploying machine learning models into production (preferred) Proficiency in programming in
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. We take a bottom-up approach to robotics and develop soft materials and devices that would enable unusual form and unconventional functions for broader robotic applications. Job description Track 1
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EPFL - Ecole Polytechnique Fédérale de Lausanne, EPFL Faculty Affairs Position ID: 2244-TTAP [#27524] Position Title: Position Type: Tenured/Tenure-track faculty Position Location: Lausanne, Vaud
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Doctoral Candidate in computer vision and machine learning for developing novel deep learning method
Machine Learning (DM3L) Doctoral Candidate in computer vision and machine learning for developing novel deep learning methods for satellite-based tracking of global CO2 and NOX emissions of point sources 80