Mentor Areas
Computational genomics, machine and deep learning for cancer research, molecular evolution, sequence analysis, microbiome
Description:
The Auslander lab at the Wistar Institute develops advanced computational and machine learning methods for cancer research. For example, we develop deep learning methods to identify new proteins in cancer, and to link infectius agents to patient outcomes.
The student will gain practical experience with different computational techniques (such as machine/deep learning, sequence analysis and computational genomics) while working on ongoing projects in the lab that are focused on improving patient diagnosis and treatment decision making.
This is a paid position, that requires on-site attending (part time, 10-20 hours weekly), not considered on-campus for OPT purposes.
Interested applicants, please contact: nauslander@wistar.org
Preferred Qualifications
Required: Python programming, basic statistics and linear algebra.
Preferred: C/C++, bash and experience with Linux HPC, tensorflow or pytorch experience, basic biology knowledge.
Project Website
Learn more about the researcher and/or the project here. https://wistar.org/our-scientists/noam-auslander
Details:
Preferred Student Year
Junior, Senior
Academic Term
Fall, Spring
I prefer to have students start during the above term(s).Volunteer
No
Yes indicates that faculty are open to volunteers.Paid
Yes
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
No
Yes indicates that faculty are open to hiring work-study-eligible students.