330 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" Fellowship positions in United States
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microbiome studies, microfluidics, or/and Machine-Learning to join our dynamic research team. The successful candidate will play a pivotal role in advancing our understanding of microbial communities through
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 10 hours ago
analyses), patient monitoring algorithms (e.g., artificial intelligence/machine learning approaches) for disease detection and management, and physiologic closed-loop controlled devices. Research activities
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across the following research areas: Predictive machine learning Robust and stochastic optimization Learning-enabled control and reinforcement learning Power system operations, planning, and electricity
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of computer graphics, human-computer interaction, computer vision, and machine learning. Conducting comprehensive literature reviews in related areas, including deep generative models, image and video synthesis
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initiatives in support of Division scientific goals · Collaborate with staff implementing advanced data pipelines, including applications of machine learning and AI for clinical prediction and identification
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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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on the effectiveness of mathematics interventions using randomized trials and other methodologies. Areas of focus might include mathematics assessment and discourse, examine the validity of research-based learning
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microscopy systems that integrate machine learning, robotic control, and real-time data analysis to achieve autonomous imaging and interpretation of complex materials systems. The Fellow will design and
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regions will be evaluated for features such as signatures of selection or diversifying or purifying selection, around genes and regions of agricultural importance. Learning Objectives: The participant will
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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