Postdoctoral Research Associate - Deep Learning, Biocomputation and Diagnostics – Cagan and Leduc Lab – Mechanical Engineering (MechE)

Updated: 7 days ago
Location: Pittsburgh, PENNSYLVANIA

Postdoctoral Research Associate - Deep Learning, Biocomputation and Diagnostics – Cagan and Leduc Lab – Mechanical Engineering (MechE)-2016089



We are seeking a Postdoctoral Research Associate - Deep Learning, Biocomputation and Diagnostics to join the Cagan and Leduc lab at Mechanical Engineering. In this role, you will carry out advanced independent and/or directed research to achieve the objectives of the research project. This position will require an in depth knowledge of a specialized field, process, subject area and may involve coordinating and implementing sophisticated research plans, the development of methods of research, testing and data collection, analysis and evaluation, and writing reports which contain descriptive, analytical and evaluative content. You will acquire the professional skills needed to pursue a career path of your choosing. The position focuses on novel approaches to digital pathology and other medical diagnostics based on feature shape and function.

The research of the Cagan lab focuses on the early design and problem solving process. Their research on computational tools, cognitive methods, and product development practice merges design theory and computational modeling. This work spans: computational representation and smart generative search; AI and Machine Learning applied to design, problem solving and diagnostics, including deep learning, agent search and rule-based induction. This also includes bio and medical diagnostics based on shape and form; cognitive and neural psychology of individuals and teams during creative design and problem solving. It designs AI/human hybrid teams, and the development of AIs that augment team performance; new technologies to result in innovative ways to solving hard engineering problems.

The research of the Leduc lab works at the intersection of mechanical engineering and biology by envisioning cells and molecules as systems that can be investigated with some of the same fundamental approaches used on machines such as planes and automobiles looking for unifying principles. These systems range from mammalian cells to microorganisms to developmental biology systems and apply principles from mechanical engineering fields to understand how these principles may apply across diverse nature-based systems.

In the energy domain, Leduc lab is passionate about algae and bacterial fuel cells. It conducts basic science and applied research in crossing over mechanical engineering approaches including confirmed mechanics, fluid mechanics, control theory, etc. with biological systems ranging from algae to artificial cells to developmental biology.

Core responsibilities include:

  • Responsible for writing software to analyze data using machine learning.
  • Writing reports and manuscripts summarizing the results of experiments.

Are you interested in this opportunity? Please apply!

Flexibility, partnership, excellence, and passion are vital qualities within Carnegie Mellon. Inclusion, collaboration and cultural sensitivity are valued proficiencies at CMU. Therefore, we are in search of a team member who is able to optimally work well with a dynamic population of partners at a high level of integrity. We are looking for someone who shares our values and who will support the university through their work.


  • Doctorate degree required in the field of engineering, computer science, or biological sciences.
  • Experience in computation and machine learning, including deep learning required.


  • Background check

More Information:

Please visit “Why Carnegie Mellon” to learn more about becoming part of an institution inspiring innovations that change the world.

A listing of our employee benefits is available at:

Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran.

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Job Function
: Research 
Primary Location
: United States-Pennsylvania-Pittsburgh 
Time Type: Full Time 
Minimum Education Level: Doctorate 

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