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materials using the recovered lignin. Learning Objectives: You will gain experience, in a research setting, by assisting with lignin fractionation experiments to include recording data, analyzing and
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computer skills, including the use of research-related software & machine learning Experience working collaboratively within a research team and sharing knowledge with others Excellent attention to detail
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science, artificial intelligence, machine learning, computational social science, data science, or a related computational discipline. Applicants must have experience with digital trace/multimedia datasets and in
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lab experiments Publish results in top-tier journals in related fields Assist in mentoring PhD students Job Requirements: PhD degree in mathematics, systems engineering, or machine learning Expertise in
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history that has taken place largely in Pakistan, and plan to return to Pakistan upon completion of the fellowship. The fellow may be invited to teach a Harvard class during the course of their appointment
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or machine learning with a particular focus on analysing large-scale behavioural data? Are you interested in developing new methodological approaches capable of extracting behavioural insight from increasingly
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– “A human-like progressive learning paradigm for embodied AI: Meta skills for robots by watching and imagination”. They will be required to: (a) identify irreducible low-level sensorimotor units
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 16 hours ago
the characterization of safety profiles and the optimization of dosing strategies — in ways that complement or improve upon conventional development paradigms. Learning Objectives: As a participant, you will have
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 13 hours ago
, our people have turned curiosity into ideas that have shaped Australia and our region. Learn more about ANU, our history and the community that has shaped us: ANU at 80 Position overview The ANU Law
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learning, and computational biology to adapt and augment existing open models for predictive biology. Approaches may include fine-tuning, parameter-efficient tuning, probing, and retrieval- or knowledge