Sort by
Refine Your Search
-
the guidance of Dr. Arash Adel, Assistant Professor in the School of Architecture and Associated Faculty of the Department of Computer Science. The desired start date is Spring 2025. Appointments are for one
-
for independent research as well as opportunities for collaboration with Princeton faculty and graduate students. Postdoctoral Research Associates may participate in the teaching program if mutually agreed, with
-
must also be comfortable working with and mentoring graduate and undergraduate student researchers. To be eligible for this position, a PhD in Mechanical Engineering, Aerospace Engineering, Chemical
-
, invites applications for postdoctoral or more senior research position for the 2026-2027 year. Renewal is contingent on satisfactory performance and continued funding. The aim of the program is to promote a
-
, lipid vesicles, polymer physics, active materials, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https
-
at Princeton University.We welcome applications from all areas in mechanical and aerospace engineering, including but not limited to the fields of: Bioengineering Combustion and Energy Science Computational
-
at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. A PhD is required
-
of squamate reptiles; the largest group of terrestrial vertebrates on Earth today with 11,000 species. A Ph.D. in Evolutionary Biology, Computational Biology, or related fields, is required. The work will focus
-
for peer reviewed publications Qualifications*Ph.D. in Environmental/Civil Engineering, Computer Science/Engineering, Data Science, or a closely related field*Proficiency in Python or other tools and ML
-
interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials