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health data, such as electronic health records or biobank-scale resources (e.g., UK Biobank, All-of-Us, FinnGen). Familiarity with machine learning approaches, such as penalised regression, deep learning
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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range of teaching and life learning programmes which address the needs of students and professional groups who are interested in and undertaking work relevant to child health. GOS ICH holds an Athena SWAN
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, single-cell analysis, and machine/deep learning (preferred but not required). Strong programming and statistical skills (e.g., Python, Perl, R, Bash). Track record of first-author research papers. Strong
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demonstrates excellent scientific, interpersonal, and communication skills. Technical proficiency, scientific creativity, collaboration with others and independent thought. To learn more and apply, please visit
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independent thinkers, curious and intrinsically motivated, with a passion for basic research. Postdoctoral fellows in the lab bring or learn diverse tools, including: Protein expression and purification
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focus of the position is on application and advancement of modern artificial intelligence (AI) methods in drug discovery and development. An ideal candidate will have strong background in machine learning
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to work within a team environment. Adaptability to a fast-paced, dynamic environment. Multitasking essential. To learn more and apply, please visit: https://careers.dana-farber.org
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interest in methods development Experience in one or more of the following areas: algorithms development, transcriptome analysis, RNA modifications, statistics, machine learning, long read RNA-Sequencing
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lines, electrophysiology, sensorimotor behavioural tests and machine learning. The project will include designing and running experiments, recording, analysing and writing up results. The postholder will