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the Energy Sciences Network or run on next-generation high performance computing and data systems hosted by the National Energy Research Scientific Computing Center at Berkeley Lab. Since its founding in 2002
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.; ●Research and develop novel generalizable deep learning computer vision algorithms for EGGIM estimation; ●Publish results in high impact journals and conferences; 3. BRIEF PRESENTATION OF THE WORK PROGRAMME
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School of Electrical Engineering and Computer Science – Faculty of Engineering Part-time (40%), fixed-term position for up to 12 months Base salary will be in the range $114,824.05 - $136,048.72
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POSTDOCTORAL COMPUTATIONAL BIOLOGIST FELLOW, under the Collins Genomics Lab to lead pioneering studies of genetic risk for cancer based on large-scale genome sequencing datasets towards the ultimate goal
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Specification PhD (or near to completion) in high energy particle physics Experience with operation and use of Roman pot-based near-beam spectrometers Experience with analysis of data from Roman pot-based near
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Institute of Systems and Robotics, Faculty of Sciences and Technology of the University of Coimbra | Portugal | 6 days ago
: Familiarization with hardware integration: training in the assembly, configuration, and operation of hardware components (sensors, computing units, communication modules) to understand their role in forest data
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leader in the research project - “Research, design, and manufacture of high-performance, fast-customizable next-generation AI-driven golf balls”. Qualifications Applicants should have: (a) a recognised
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imaging, head & neck imaging) and development of novel imaging techniques (nuclear and molecular imaging, computational imaging, etc). With the formation of the National University Health System (NUHS
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single cell sequencing data and high-performance computing environment is highly preferred. A good understanding of cancer biology, immunology, cell biology, and molecular biology is also preferred. Proven
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heat and mass transfer, thermal comfort, and computational fluid dynamics (CFD). These skills will be applied to design, evaluate, and optimise strategies that enable the effective implementation of MMV