-
such as protecting embedded systems from vulnerabilities rooted in sensor physics, studying the impact of physical signals (e.g., acoustics, lasers, electromagnetic emissions) on AI and sensing systems, and
-
existing and new computational methods to study multiscale processes in catalysis and photochemistry. Other projects that align with our general interest are also possible. Please see https://sdonglab.org
-
science/engineering, physics, or related fields. This candidate should have a strong background in instrumentation prototyping, particularly with experience on building optical imaging systems. Experience in near
-
embedded systems from vulnerabilities rooted in sensor physics, studying the impact of physical signals (e.g., acoustics, lasers, electromagnetic emissions) on AI and sensing systems, and innovating hardware
-
Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 07-Oct-25 Location: Boston, Massachusetts Type: Full-time Categories: Academic/Faculty Physical Sciences Internal
-
such as mathematics, statistics, physics, computer science, engineering, epidemiology, or related disciplines; Strong background and experience in mathematical and computational modeling of infectious
-
://tanglab.sites.northeastern.edu/ Qualifications: Having a PhD degree from all science and engineering majors, especially Mechanical Engineering, Chemical Engineering, Physics, and Materials Science. Highly motivated. Having
-
. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to machine learning. Ph.D. in Materials Science, Physics
-
to disseminate research results QUALIFICATIONS Applicants must have (or be about to receive) a PhD in physics, biophysics, systems biology, applied mathematics, bioengineering, chemistry, chemical engineering, or
-
. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to machine learning. Ph.D. in Materials Science, Physics