cancer cells to yield cancer and immune biology that is almost entirely unknown.
To dissect these events, we employ a systems biology, top-down approach where advanced analytics of large datasets are coupled to focused perturbations (ex: shRNA, CRISPR, cDNA screens) in experimental model systems (e.g. organoid & cell line models). We also apply a bottom-up approach where we employ cutting edge methods to dissect novel model systems we develop (ex: mouse models).
Some of the techniques our group develops and applies are:
- advanced sequencing technologies, at single cell and bulk levels, to generate large multi-modal datasets.
- novel algorithms & computational approaches, based on maths, statistics, & machine learning, to nominate novel cancer therapeutic targets.
- innovative, molecularly engineered model systems in in-vitro and in-vivo settings (e.g. mouse models)
- highly parallel perturbation approaches; CRISPR, shRNA, PROTACs, small molecules
We aim to advance:
- novel therapeutics; targeted as well as immunotherapies
- early diagnostic as well as prognostic tools
- novel technologies that can be leveraged to address outstanding biological questions
Disease areas of focus include, but are not limited to, acute leukemias, sarcomas, as well as breast and pancreatic cancers.
The training/mentorship opportunities are in the following areas (among others):
- Developing computational and chemical biology approaches to advance copy number informed precision therapies.
- Studying cancer-immune cell interactions during cancer initiation and progression using functional biology approaches & advanced in-situ sequencing/data analytics.
- Developing machine learning approaches to advance novel prognostic and diagnostic methods in cancer.
- Dissecting cancer genome evolution using the first of their kind, somatic lineage tracing mouse models, along with human data analysis.
- Developing single-cell as well as circulating tumor free DNA sequencing technologies.
- Designing and applying novel comparative oncogenomic algorithms to advance unifying principles of chromosomal biology in cancer.
Prospective trainees can expect the followings: Mentorship, Freedom, Resources, Exciting Science, & Fun.
Further details can be found here: http://baslanlab.com
Qualifications
(1) A proven record of publication (this can include bioRxiv and/or arXiv papers, or about to publish),
(2) For experimentalists, skills in one, or all of the following areas are required: molecular biology, cell culture, and animal work, for computational positions, skills in one or all of the following areas are required: next generation sequencing data analysis, statistics, and machine learning.
Start date and term are negotiable.
Application Instructions
Candidates should have a Ph.D. Applicants are required to submit the following materials through Interfolio:
- CV
- Cover letter
- A list of 3 referees
Questions or follow up inquiries can be emailed to Dr. Baslan at tbaslan@upenn.edu
To apply, visit https://apply.interfolio.com/194436
The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.
