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collaborate with MIP.labor fellows to develop new science journalism formats related to current research in mathematics, computer science, and physics. Research and practice fuel each other. To manage and
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EUR gross yearly) and full social security coverage. Proposals are invited in the following fields: Biology; Neuroscience; Physics; Chemistry; Computer Science; Data Science; Mathematics; Earth Science
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, or computer science, or MD with comparable research experience Considerable experience in computer programming and computational biological applications. A strong background in statistics and biology. Experience
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to candidates with a strong quantitative background. We welcome applicants with training in mathematics, statistics, health economics, computer science, or epidemiology, particularly those with good numeracy and
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; Neuroscience; Physics; Chemistry; Computer Science; Data Science; Mathematics; Earth Science; Astro sciences; and related disciplines Application deadline: September 18 (14:00 CEST) Salary: three-year fully
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across science, technology, engineering, and mathematics includingmedicine (STEM). Through AI4X-PDF, we aim to cultivate the next generation of research leaders working at the intersection of AI and STEM
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and innovation (R&I) ecosystem. This is a unique opportunity to establish or expand your research programme in the UK, collaborate with world-class researchers, and contribute to national and global
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) discipline (includes computer science and mathematics). Secure a supervisor (and co-supervisor, if applicable) who has a full-time budgetary tenure-stream appointment or a status-only appointment at
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projects that leverage our optical expertise to probe brain function Preferred Qualifications & Expertise: PhD in neuroscience, bioengineering, computational biology, or related field At least one strong
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organoids will be plus. Dry lab: Highly motivated candidates with a PhD/MD degree in bioinformatics, genome science, systems biology, biomedical informatics, computational biology, machine learning, data