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of maintaining quality standards within a highly structured laboratory unit. About The Role The Senior Research Fellow will be required to develop and lead a research programme in the DCC that complements and
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Details Title HMS - Postdoctoral Fellow in Biomedical Informatics (DBMI) School Harvard Medical School Department/Area Biomedical Informatics-Quad Position Description We invite applicants for a
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Application Deadline 24 Sep 2026 - 00:00 (Asia/Nicosia) Country Cyprus Type of Contract To be defined Job Status Full-time Hours Per Week 40 Is the job funded through the EU Research Framework Programme
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design, data collection, and analysis. Both workshop will be held in one of the project case study countries Essential Criteria PhD in Social Sciences (applicants undertaking PhD and nearing completion
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Mentorship: Co-supervise students and research assistants with structured feedback on study design, analysis, and writing. Model responsible conduct of research and mentorship best practices. Research program
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) Application Deadline 17 Oct 2026 - 00:00 (UTC) Country Australia Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the
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to build ‘design-build-test-learn’ pipeline through computational design and experimental validation. About you PhD (or in the final stages of PhD submission) in computational biology, evolutionary genomics
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the applicant’s PhD (incremental transitions will not be considered). Open-ended computational/theoretical projects: Collaborate flexibly with multiple research groups to investigate human cognition through
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Skills: Proficiency in Python, TensorFlow, PyTorch, or similar frameworks. MS or higher degree (PhD preferred) degree in Computational Neuroscience, Computer Science, Bioinformatics, or a related field
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collaborative and interdisciplinary activities. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Applied Mathematics or equivalent. Strong academic background in machine learning