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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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biostatistical methods to better personalize treatment of cancer patients from deep and diverse types of biomedical information. Analysis of large‑scale clinical, genomic, and molecular datasets. Development and
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., R, Python, SAS, or STATA) Experience working with large population dataset (e.g. EHR, claims data) Background in health informatics, population health research, or biostatistics, computer science
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make a difference in the world! Position Information The Postdoctoral Research Associate is responsible for assisting in the development and application of large-scale modeling frameworks for water and
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, fluorescence lifetime and vibrational imaging methods. Large-scale and high-throughput imaging and analysis pipelines: data acquisition and analysis to characterize cell-types and connectivity in mammalian brain
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team. · Applies statistical methods to large-scale, longitudinal training-related behavioral data. · Maintains substantial knowledge of state-of-the-art principles and theories
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8 Apr 2026 Job Information Organisation/Company ENSEMBLE3 Sp. z o. o. Research Field Physics » Chemical physics Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Application
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background in statistics, and familiarity with large data sets such as metabolomics or epigenetic sequencing. Experience in epigenetics or gene regulation is a plus. Experience with statistical analysis tools
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 25 days ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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communication skills in both English and Chinese; (d) have strong empirical research skills with expertise in causal inference methods (e.g., quasi-natural experiments, field experiments); (e) be proficient in big data