Postdoctoral researchers
A postdoctoral researcher position is available in the laboratory of Dr. Daifeng Wang (https://daifengwanglab.org/ ) in Waisman Center and Department of Biostatistics and Medical Informatics at the University of Wisconsin – Madison. The aim of this position is to develop and apply interpretable machine learning approaches such as deep neural network to analyze large-scale multi-omic data for understanding functional genomics in the human brain, especially for neuropsychiatric, neurodevelopmental and neurodegenerative diseases. The selected candidate will work in an interdisciplinary environment and have chance to collaborate with PIs from Waisman Center, UW-Madison and other top institutes. She/he will be able to contribute to several on-going projects and participate in the lab development and grant applications.

The applicant should have a PhD or equivalent degree in bioinformatics, computer science, data science, engineering, biology, physics or related areas, and be skilled in programming (e.g., R, Python). Prior experience on next-generation sequencing data, bioinformatics software development, machine learning is preferred. Prior background in neuroscience or genomics is a plus, but not required. Applicants are requested to send a CV and a list of 3 references to daifeng.wang@wisc.edu .

Ph.D. students
Graduate research assistant positions available for Ph.D. students. Potential graduate students can apply the graduate program in Biomedical Data Science at University of Wisconsin-Madison, and indicate interest in this lab. Contact information is here.

MS students
Research projects are available for MS students. Contact information is here.

Undergraduate students
We have a number of projects in data mining, machine learning, bioinformatics and data science for motivated undergraduate students. Contact information is here.
Select projects:
So Yeon Min was a junior undergraduate student from MIT EECS and funded by Google Summer of Code 2017. Her GSoC project developed a Web App for Identifying Gene Expression Biomarkers to Classify Cancer Patient Outcomes [github, youtube demo].