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machine learning, computer vision, human-computer interaction, or similar relevant areas. Experience in research or development on bias, interpretability, and/or privacy in machine learning/AI is necessary
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will provide guidance to less experienced members of the research group, including postdocs, research assistants, technicians, and PhD and project students. Key responsibilities: • Manage own
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning
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influence clinical practice. We welcome applications from candidates with following backgrounds: Candidates with strong experience in medical image analysis, machine learning (especially deep learning) and
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programming), (2) populating agent-based models with realistic agent behaviours (e.g. using machine learning techniques), (3) calibrating large-scale agent-based models and (4) validation and verification
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Machine Learning, Human-Computing Interactions, Social Sciences, and Public Health. Applicants should hold, or be close to completion of, PhD/DPhil with research experience in computer science, statistics
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opportunity to teach. Applicants should hold, or be close to completing, a PhD in plasma physics or high-power laser-plasma interactions. They should have extensive experience of working with particle-in-cell
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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certification in data science, machine learning, or analytics, expertise in immunohistochemistry and digital imaging techniques. Previous experience working in a molecular or biochemistry laboratory and/or prior
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dissemination and grant writing. About you You will hold a PhD (or be close to completion) in a relevant field, in addition to experience of implementing or fine-tuning LLMs using machine learning libraries