Postdoctoral Researchers, Baslan Lab

University of Pennsylvania; Pennsylvania; On-site; USD 55,000 - 65,000; Full time

Job summary

The Baslan Lab at the University of Pennsylvania's Department of Biomedical Sciences invites applications for five Postdoctoral Fellow positions (computational & experimental). The group studies cancer genomes focusing on Copy Number Alterations, using systems biology, sequencing, and model organisms.

Candidates should have a Ph.D. and strong publication records; experimentalists should have molecular biology, cell culture and animal work experience; computational folks should have NGS data

Qualifications

• Ph.D. required; postdoc candidates must have strong publication record.
• Experimentalists: molecular biology, cell culture, and animal work.
• Computational candidates: next-gen sequencing data analysis, statistics, and ML.
• Open to US citizens and foreign nationals.

Responsibilities

• Perform computational and experimental studies on cancer CNA biology.
• Develop and apply multi-omics analyses and model systems.
• Collaborate with multidisciplinary team and contribute to publications.

Skills

Molecular biology

Cell culture

Animal work

Next-generation sequencing data

Statistics

Machine learning

Education

PhD in a relevant field

Tools

CRISPR

shRNA

PROTACs

Job description

Faculty Mentor: Timour Baslan, Ph.D.

Department: Biomedical Sciences

Positions are funded by various grants: federal as well as foundation.

Five positions

The positions are open to US citizens and foreign nationals.

The Baslan Lab at the Department of Biomedical Sciences, the University of Pennsylvania (Penn) is recruiting Postdoctoral Fellows (computational & experimental), 5 positions in total, 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 following: Mentorship, Freedom, Resources, Exciting Science, & Fun.

Further details can be found here: http://baslanlab.com

• A proven record of publication (this can include bioRxiv and/or arXiv papers, or about to publish),
• 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.

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 [e-mail]

Equal Employment Opportunity Statement

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.