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Postdoc Position for Computational Genomics, AI Precision Oncology, Cancer Biology and Immunobiology
University of Pittsburgh, Pittsburgh , Pennsylvania, US | Pittsburgh, Pennsylvania | United States | 2 months ago, IMPREG signature) for precision immuno-oncology panels. Preferred qualifications: Ph.D. in bioinformatics, computational biology, computer science, statistics, or a related field. Strong programming skills
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or similar. Running social simulations with AI agents using platforms like Concordia or similar. Coding in Python or a similar language for simulation, visualization, and advanced statistical analysis (causal
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results-oriented team player. Ideal profile includes following competencies and experience: Strong programming skills, deep statistical knowledge and a proven track record with machine learning Solid
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strong foundations in operations research/optimization modeling and econometric/statistical/ml analysis with a passion for applied problem solving. The work will involve large-scale data analysis and work
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related tasks. Responsibilities: 1) Biomedical data analysis (40%): Statistical analysis of biomedical data by using existing methods and developing custom code scripts. 2) Bioinformatics method development
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and Statistics (11 ) Physics (16 ) Science & Engineering-related (2 ) Social and Behavioral Sciences (28 ) Age: Must be 18 years of age Affirmation I certify at the time of application that I
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Sciences (14 ) Life Health and Medical Sciences (48 ) Mathematics and Statistics (11 ) Physics (16 ) Science & Engineering-related (2 ) Social and Behavioral Sciences (28 ) Age: Must be 18 years
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collaboration, this position is based on campus, full-time, at Harvard University. Remote work for this position is not possible. Basic Qualifications PhD in computer science, statistics, electrical engineering
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related tasks. Responsibilities: 1) Biomedical data analysis (40%): Statistical analysis of biomedical data by using existing methods and developing custom code scripts. 2) Bioinformatics method development
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or willingness to develop skills in analytical techniques such as GC–MS or related methods for quantifying seed-applied compounds Experience with statistical modeling, experimental design, and multivariate