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addition to genetics, we seek candidates who can teach (1) introductory courses in data science using R or Python or a general biostatistics course, and (2) 200 or 300-level courses in their area of expertise. Ideally
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curATime (Cluster for Atherothrombosis and Individualized Medicine ; https://curatime.org/ ) a Clusters4Future initiative funded by the German Federal Ministry of Research, Technology and Space (BMFTR
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field. • Preferred experience in computational biology, single‑cell perturbation data analysis, and programming in Python or R. • Interest in machine learning, cancer genomics, and single‑cell
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in quantitative modeling, data science, mechanical and urban engineering, or AI/ML methods as exemplified by a strong publication record. Previous experience in Python-based scientific computing
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health, or related field, plus at least two years directly related experience required. Advanced knowledge and proficiency in statistical programming (e.g., R, SAS, Python, Stata) preferred; prior
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Job description The Affinity Proteomics unit (https://www.scilifelab.se/facilities/affinity-proteomics/ ) is part of
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-cell RNA-seq, bulk RNA-seq, proteomics, etc. Extensive knowledge of technologies used in support of biomedical research, such as programming languages (R and Python), databases (SQL), version control
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Methods for Partial Differential Equations and Functional Analysis, programming skills (e.g., Matlab or Python), international experience, and excellent proficiency in written and spoken English are
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: Proficiency with R, Python, or Stata. Work with large and complex datasets. Apply machine learning algorithms in operational environments. Proficient with GitHub. Worki on field experiments / randomized
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cognitive search. Develop Snowpark Python transformations, UDFs, and machine-learning features. Implement vectorized storage, model-serving patterns, and AI-ready data transformations. Support RAG/semantic