Postdoctoral Fellow (computational & experimental)

The Baslan Lab at the Department of Biomedical Sciences, the University of Pennsylvania (Penn) is recruiting  Postdoctoral Fellows (computational & experimental)to join our multidisciplinary team of investigators. Our work focuses on studying cancer genomes, with a specific attention to the genetics and biology of a cryptic class of cancer associated mutations termed Copy Number Alterations (CNAs). These somatic events change the number and composition of chromosomes in 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: 

Interested applicants are expected to have:
(1) A proven record of publication (this can include bioRxiv and/or arXiv papers, or about to publish),
(2) Passion and dedication to perform science

Applications are requested to email the following material to Dr. Baslan at  View email address on nature.com :
- CV
- Cover letter
- a list of 3 referees

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