Full-Time Research Assistant

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The Liu Lab in the Department of Biostatistics at Yale University develops statistically principled, AI-powered methods for large and complex biomedical data. Our work connects genetic variation, molecular regulation, perturbation response, and disease phenotypes to advance mechanistic understanding and causal discovery. The lab offers a collaborative environment spanning AI, statistics, genomics, and public health, with access to Yale's large-scale GPU computing resources.

Work closely with Dr. Qiao Liu and interdisciplinary collaborators on projects at the intersection of generative AI, causal inference, and regulatory genomics. Responsibilities include preprocessing and analyzing genomic and biomedical datasets; implementing and evaluating machine-learning and statistical methods; running computational experiments on Yale HPC/GPU systems; maintaining clear, reproducible code and documentation; summarizing relevant literature and results; and contributing to figures, presentations, and manuscripts.

The RA will receive mentorship and opportunities to develop skills in AI, causal inference, and computational genomics.

Bachelor's or master's degree in computer science, statistics, biostatistics, computational biology, bioinformatics, data science, or a related field. Strong Python programming skills are expected. Experience with PyTorch or Tensor Flow and Linux/HPC environments is preferred. Research experience in machine learning, generative modeling, causal inference, or computational genomics is an advantage. We value intellectual curiosity, careful and independent work, strong communication and teamwork, and enthusiasm for interdisciplinary biomedical research.

See benefit from Yale official website: (Use the "Apply for this Job" box below).-compensation

We prefer an RA candiate, who is expected to join Liu lab very soon.