Positions

Postdoctoral Fellowships

The Gülhan Lab is seeking multiple postdoctoral fellows to work on innovative computational methods in the research areas of interest of the lab.

The requirements are a Ph.D. in bioinformatics, computational biology, or relevant degrees (such as mathematics, physics, and computer science), an excellent track record of programming skills, and publications. Additionally, medical research fellows with experience in bioinformatics are encouraged to apply for the translational projects. Postdocs will get the chance to be a part of supportive and inclusive communities such as the Bioinformatics team at the Krantz Family Center for Cancer Research at MGH and the Department of Biomedical Informatics at Harvard Medical School. They will work closely with clinical teams at the forefront of precision oncology. Additionally, we accommodate remote or hybrid positions.

Positions are available immediately. To apply, send a cover letter, resume, name, and contact details of two references to dgulhan@mgh.harvard.edu.

Below is a list of example projects:

Mutational signature analysis:

Building innovative signature analysis methods addressing some of the limitations of existing approaches.

Improving accuracy and interpretability of signatures by deconvoluting their temporal and genomic locus dependence.
Applying these methods in large datasets and by building an effective multi-study discovery strategy.
Comprehensive characterization of genomic instability mechanisms and patient classification through multi-modal signature analysis methods.

Liquid biopsy tests with cell-free DNA:

Building a comprehensive set of algorithms for mutation, tumor detection and gene expression inference methods; Utilizing them in development of an early-cancer detection cell-free DNA test and studying drug resistance mechanisms.

High accuracy single-nucleotide variant and indel detection through the use of signatures and machine learning
Building fragmentomics model for improved CNV and tumor fraction inference
Enhanced tumor detection using mutations and fragmentomics jointly
Gene expression inference methods

Long read single-cell sequencing:

Developing tools for joint genome and transcriptome profiling and using it to study early cancer development mechanisms and dynamic shifts in cancer cell composition in pre-/post-treatment biopsies in patients treated with targeted therapies.

Calling copy number variants, single-nucleotide variants, and indels from long-read scRNA-seq data. Studying mutational processes and their intratumor heterogeneity
Multi-modal characterization of single cells to detect cancer cells in precursor lesions
Multi-modal characterization of changes in molecular characteristics of cellular composition of tumors in highly heterogeneous metastatic tumors
Studying haploinsufficiency of immune cells in DNA-repair gene mutation carriers

Postdoctoral Fellowships

Students