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Field
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to produce resilient and high-performing models. · PhD in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field Strong track record of applying ML in academic or industry
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engineering, and hyperparameter tuning to produce resilient and high-performing models. · PhD in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field Strong track record
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Job Description Apply now Job Title: Postdoctoral Fellow PhD Division: Neurology Work Arrangement: Onsite only Location: Houston, TX Salary Range: Hiring up to $62,232 FLSA Status: Exempt Work
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Job Description Apply now Job Title: Postdoctoral Fellow PhD Division: Neurology Work Arrangement: Onsite only Location: Houston, TX Salary Range: Hiring up to $62,232 FLSA Status: Exempt Work
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smart technologies in the bioeconomy, agricultural, food and environmental policy impact analysis, and/or production efficiency and food markets. leading to an internationally competitive PhD degree and
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, professionals, service user and policy makers. The successful candidate will pursue their own doctoral research project leading to a PhD within a collaborative research environment. We seek applicants with a
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considered. Qualifications/Skills PhD degree in a programme relevant to human-computer interaction and/or critical computing, ideally in Computer Science, Industrial Engineering, Interaction Design, or a
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) Developing machine-learning based exoskeleton controllers to work across tasks 2) Designing and validating new robotic lower-limb prostheses 3) Exploring other high-risk high-reward research areas related
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 6 hours ago
and cell culture techniques are desired. The candidate is expected to work closely with an interdisciplinary research team and must be motivated to acquire new experimental skills. PROJECT DESCRIPTION
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, at the University of Cambridge, UK. The Postdoc will work together with a team of students and research collaborators on the development of learning-based discovery of robot task/environment designs