The Aizenberg Lab at Harvard University is seeking a postdoctoral researcher to join a multidisciplinary team working on SNIFFIA – the Sensory Nature-Inspired Fact Finder of Indoor Air. This ambitious project aims to revolutionize odor sensing by developing biologically inspired, AI-enhanced artificial noses capable of real-time chemical detection and adaptive sampling.  This role will focus on the design, fabrication, and integration of selective and adaptive gas filtration materials as part of a new platform for indoor air quality sensing. The researcher will collaborate closely with experts in AI, materials science, and microfluidics to create a modular, tunable system for complex analyte analysis.  Subprojects include:

1. Development of Highly Specific Gas Sensors This subproject focuses on designing and fabricating nanostructured materials capable of selectively filtering out interferent gases, thereby enhancing the specificity and sensitivity of indoor air quality sensors. The work will involve leveraging advanced nanofabrication techniques such as electron beam lithography, nanoimprint lithography, and soft lithography to create porous and hierarchical architectures tailored for specific molecular interactions. These filters will be integrated into gas sensor arrays to ensure compatibility with real-time sensing platforms. The goal is to construct sensor modules that can operate effectively in complex chemical environments and distinguish between target analytes and environmental background noise. 

2. Design of Adaptive Filtering Materials A key innovation in this project involves the development of dynamic, responsive materials that can actively adjust their filtering properties based on environmental cues or programmed inputs. This will include work with materials such as inverse opals, stimuli-responsive hydrogels, or other adaptive systems that can change pore size, wettability, or chemical affinity when triggered by stimuli like humidity, temperature, light, or electrical signals. These adaptive filters aim to mimic natural sniffing behaviors by tuning their selectivity in real time, improving the system's ability to detect transient or low-concentration analytes. Such dynamic behavior will be critical for creating smarter, more energy-efficient artificial nose systems. 

3. Development of Modular Selective Filters This subproject aims to create a set of reconfigurable, modular filter units that can be deployed on-demand depending on the sensing context. These modules will be designed to be cost-effective, scalable, and suitable for integration into portable or distributed indoor air quality monitors. Prototyping will involve rapid fabrication approaches and testing a range of material formulations to balance selectivity, permeability, and durability. The researcher will evaluate the filters' performance under realistic indoor conditions, including variable humidity, temperature, and the presence of multiple VOCs or particulates. The end goal is to produce a flexible library of filter components that can be easily swapped or adjusted to optimize sensing for different environments and use cases.  

Preferred start date as soon as possible but flexible. 

Documents should include a full CV, cover letter summarizing your experience, list of reference contacts, and up to 3 publications.

PhD in Materials Science, Applied Physics, Applied Mathematics or related fields by the time the appointment begins.

Strong communication skills, motivation in scientific research, and experience in writing scientific papers are required. Candidates with a strong background in solid-state batteries or unconventional superconductors are encouraged to apply.  

Marina McLaughlin

Applicants should include a full CV, cover letter summarizing your experience, list of reference contacts (minimum of 3), and up to 3 publications.

Harvard is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status.