Postdoctoral Scholar Opportunity

Postdoctoral Scholar – Research Associate
Department: Convergent Science Institute in Cancer, USC Dornsife College of Letters, Arts & Sciences
Location: Los Angeles, California 

The University of Southern California (USC), founded in 1880, is located in the heart of downtown L.A. and is the largest private employer in the City of Los Angeles. As an employee of USC, you will be a part of a world-class research university and a member of the “Trojan Family,” which is comprised of the faculty, students and staff that make the university a great place to work. 

About CSI-Cancer
The USC Michelson Center Convergent Science Institute in Cancer (CSI-Cancer) brings together engineers, data scientists, biologists and clinicians to tackle fundamental and translational problems in cancer—from mechanistic modeling and physical dynamics of metastasis to high-content single cell data and advanced machine learning. Our vision is to integrate patient, model system and high‐dimensional data to achieve a mechanistic understanding of cancer dynamics across scales. We are studying circulating tumor cells (CTCs) and other analytes in liquid biopsies (blood, bone marrow aspirates, and other liquids) of cancer patients as an approach to complement cancer care. Our data science driven single-cell multi-omic approach allows us to look at the morphology, proteome, and genome of these CTCs individually. 

Position Overview
We are seeking a highly motivated Postdoctoral Scholar with a strong background in biomedical engineering (BME), computer science (CS), or biophysics, who is comfortable working at the interface of software and hardware with primary emphasis on software and a secondary emphasis on hardware. The successful candidate will join a convergent research team focused on early detection, liquid biopsy, rare-cell detection assays, and the development of novel computational and instrumentation platforms for cancer research. They will develop and maintain data analysis methods and process pipelines to support the research, data, and analytic infrastructures of the institute (https://kuhn.usc.edu/liquid-biopsy/). 

Key Responsibilities 

• Develop and implement advanced software/data-analysis pipelines, including image/slide-based workflows (segmentation, representation learning, outlier detection) for rare cell and biomarker detection in liquid biopsy contexts. 
• Collaborate in the design, prototyping and integration of hardware/instrumentation components (e.g., microfluidic systems, imaging modules, sensor systems) to support novel assays and measurement technologies; ensure that data generated are optimized for robust downstream software analysis. 
• Integrate software and hardware workflows to enable seamless data acquisition, robust processing, analysis, and interpretation of cellular datasets. 
• Work closely with multidisciplinary team members (biologists, clinicians, engineers, data scientists) to define requirements, translate experimental needs into computational/hardware solutions, and iterate on design. 
• Supports continuous improvements by maintaining currency with new technologies and leveraging the latest industry knowledge to continually develop skills, knowledge, and abilities. 
• Makes technical presentations and demonstrations at conferences and/or meetings. 
• Publish high‐impact peer‐reviewed papers and contribute to grant proposals and intellectual property initiatives. 
• Mentor junior staff and trainees. 
• Performs other related duties as assigned or requested. The university reserves the right to add or change duties at any time. 

Preferred Qualifications 

• Experience with one or more of: high‐throughput microscopy, whole-slide imaging, single‐cell data (e.g., flow cytometry, mass cytometry, imaging cytometry). 
• Demonstrated ability to build robust, scalable computational pipelines for single cell or rare‐event detection. 
• Ability to write high-quality Python, R and SQL code and experience in open domain (e.g., GitHub). 
• Familiarity with cloud computing or high‐performance computing environments for large‐scale data processing. 
• Previous collaborative work across biology/engineering/computation domains. 
• Published writing on artificial intelligence and/or data science a plus. 
• Demonstrated ability to take initiative and lead sub‐projects or components of research programs. 

Minimum Qualifications 

• A PhD in Biomedical Engineering, Computer Science, Biophysics, or a closely related discipline. 
• Strong software development skills (e.g., Python, C++/C, R, Java, or comparable) and familiarity with data science/machine learning frameworks. 
• Some experience (or strong interest) in hardware/instrumentation development. 
• Demonstrated understanding of advanced computational methods such as contrastive deep learning. Knowledge of current data modeling tools and various machine-learning techniques and algorithms (e.g., clustering, decision-tree learning, artificial neural networks). 
• Proficient use of query languages (e.g., SQL, MDX) and experience working with relational (e.g., MySQL, PostgreSQL) databases. Knowledge of statistical concepts and techniques (e.g., distributions, statistical testing, regression). 
• Excellent problem‐solving skills, strong written and verbal communication, and ability to work productively in a multidisciplinary team. 
• A track record of scientific publications. 

Appointment Terms 

• Full‐time appointment as a Postdoctoral Scholar. 
• Typical appointment duration: 1–2 years initially, with possibility of renewal subject to performance and funding. 
• This position is not eligible for fully remote work. 
• This is a grant-funded position. 

Application Instructions

Please submit the following materials to Dr. Peter Kuhn at kuhn42@usc.edu: 

1. Cover letter. 
2. Curriculum vitae, including list of publications. 
3. Contact information for three references. 
4. A one‐page summary of a recent micro-project you led (software or hardware) and your role in it.