Sort by
Refine Your Search
-
Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 13-Nov-25 Location: Nahant, Massachusetts Type: Full-time Categories: Academic/Faculty Physical Sciences Internal
-
· Assistive technologies for individuals with visual impairments · AI-driven physical therapy and rehabilitation systems · Human-robot interaction and coordination Responsibilities: Perform
-
projects independently and collaboratively Demonstrated ability to mentor students and manage complex projects Experience with water resources and flood-resiliency planning is a plus. Hiring Process The
-
until filled. The initial appointment is for one year, with the potential for extension depending on regular progress evaluation and continuation of funding. QUALIFICATIONS A Ph.D. in chemistry, physics
-
indication of our preferences around the discipline or background of a successful candidate. The hiring process for this role is ongoing. Application review will begin immediately, and candidates will be
-
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
-
proteomics analysis Organize, process, and report experimental data. Present findings in lab meetings and contribute to manuscript and report writing. Collaborate across functions and mentor junior lab members
-
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
-
Ph.D. in Applied Mathematics or Mathematics or Physics; be able to undertake substantially full-time research or scholarship; work under the supervision of a senior scholar. The postdoctoral fellow
-
such as mathematics, statistics, physics, computer science, engineering, epidemiology, or related disciplines; Strong background and experience in mathematical and computational modeling of infectious