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Field
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, and decision support. Qualifications Required: PhD in Computer Science or a related field, with a focus in databases, data systems, theory, or algorithms. Strong publication record in top-tier venues
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expertise in robotics, sensing, machine learning, biomechanics, and haptics. The project is funded by a NWO VIDI grant to the PI and provides financial security, access to state-of-the-art equipment, travel
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of the art data science approaches (text mining, machine learning, AI) to comprehensively highlight yet undiscovered virus/host/environment relationships and annotate potentially putative new spillover
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. This is part of your personality: Completed doctoral/PhD studies in the field of chemistry, pharmacy and similar fields Excellent knowledge in computer-aided drug design and medicinal chemistry Publications
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, physical therapy, speech, language and hearing sciences, health science, human physiology, behavior and health, and nutrition. Applicants may learn more about professional development opportunities, benefits
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or commensurate with experience Type of Position Student Position Time Status Full-Time Required Education PhD or MD Required Related Experience Biomedical Sciences Required License/Registration/Certification NA
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-$60,000 Type of Position Student Position Time Status Full-Time Required Education PhD Required Related Experience N/A Required License/Registration/Certification N/A Physical Requirements Check all
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for candidates to have the following skills and experience: Essential criteria PhD qualified in mathematical, physical or computational sciences Experience in using machine learning methods to analyse datasets
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analysis and visualization, signal processing, and ideally machine learning. • Working knowledge of Distributed Acoustic Sensing (DAS) and its applications in seismology (appreciated). • Aptitude
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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | about 1 month ago
(denoising, Mueller matrix calculation/decomposition) and AI-based diagnostic algorithms using machine/deep learning. The primary challenge will be to deliver practitioner-relevant cervical images in under 0.5