Postdoctoral Fellow

The Department of Urology is seeking a highly motivated and talented postdoctoral scholar to join the urogenetics research group led by Dr. Chen-Han Wilfred Wu. The Wu Lab operates at the forefront of Precision Surgery, Genetics, and Big Data Analysis. This position is ideal for a Computational Scientist, Bioinformatician, Genetic Epidemiologist, or Statistician eager to leverage large‑scale genomic and clinical datasets.

The successful candidate will investigate the genetics and phenotypic basis of urological conditions, including kidney stone disease, male infertility, congenital anomalies of the kidney and urinary tract (CAKUT), and genitourinary cancers.

• Data Analysis:
  Perform advanced genomic and phenotypic analyses, including the processing of big data such as high‑throughput sequencing data (e.g., WES, WGS, RNA‑seq), electronic medical records, and large‑scale biobank data (e.g., UK Biobank, All of Us).
• Algorithm Development:
  Develop and implement robust computational pipelines to integrate multi‑omic data with clinical phenotypic information from EMR.
• Research Integration:
  Collaborate with wet‑lab investigators to provide computational insights and cross‑validate findings derived from dry‑lab analyses.
• Publication & Presentation:
  Lead the preparation of high‑quality manuscripts for peer‑reviewed journals and present findings at major national and international conferences (e.g., AUA, ASHG, ACMG).
• Grant Support:
  Assist in drafting research proposals and generating preliminary data for NIH and other federal funding applications.
• Mentoring:
  Supervise and mentor junior trainees, including medical, graduate, and undergraduate students as appropriate.

• Education:
  
  Ph.D. in Genetics, Genomics, Bioinformatics, Computational Science, Statistics, or a related quantitative field. An MD/DO with a strong computational research background will also be considered.
• Technical
  
  Skills:
  • Proficiency in programming languages, such as R or Python.
  • Extensive experience with Linux/Unix environments and High‑Performance Computing (HPC) clusters.
  • Experience in Biobank‑scale research (e.g., UK Biobank, All of Us).
  • Familiarity with standard genomic tools (e.g., GATK, bcftools, samtools, PLINK, VEP, ANNOVAR).
  • Expertise in monogenic disease analysis or statistical genetics (e.g., GWAS, PheWAS, Polygenic Risk Scores).
• Communication:
  Excellent written and verbal communication skills in English.

Pre‑employment background check investigation is performed on candidates selected for employment.

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