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Field
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mentor, the participant will: Acquire or improve their ability to predict important plant-microbe associations based on computational data and supplemental bioassays; Learn to conduct research using
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learn and apply methods in computational biology, genetics, and artificial intelligence, including statistical methods of marker-trait association, methods for determining syntenic relationships and for
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What You’ll Need: PhD in computer science, artificial intelligence, machine learning, computational biology, biomedical engineering, or a closely related quantitative field. Strong foundation in modern
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of Computer Science and affiliated with the Information Systems and Human–Computer Interaction (ISCHI) research group. Your immediate leader will be the unit leader of the Information Systems and Human–Computer
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
processing, computer programming, and fieldwork are encouraged to apply. The successful candidate will be a member of the Geophysics Department based at RSES. RSES is Australia’s leading academic research
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Only Degree: Doctoral Degree. Discipline(s): Chemistry and Materials Sciences (2 ) Computer, Information, and Data Sciences (1 ) Engineering (2 ) Life Health and Medical Sciences (17
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Fusion Tribrid MS and Waters Q-ToF instruments are highly desired. Experience handling and analyzing large-scale MS, MS(MS) and/or proteomics-like datasets using statistical and machine learning techniques
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed
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. Expertise in artificial intelligence and machine learning. Recent research experience in the development of first-principle wave models. Recent research experience in the development of numerical codes
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phenotyping using both drone-based and ground based sensing platforms. Learn artificial intelligence and machine learning techniques to analyze image and geospatial data from diverse sources for crop monitoring