Postdoctoral Associate in Mobility and Infrastructure ...

REQUIRED: Ph.D. in engineering, computer science, mathematics, statistics, physics, or related field; experience with signal processing, spectral analysis, time-series analysis, handling large-scale datasets, and the modeling and analysis of complex systems; strong publication record; proficiency with either MATLAB or Python and practical skill with both; and strong oral and written English communication skills. PREFERRED: experience with C++, Java, Hadoop, MapReduce, SQL, and other relevant programs; experience with applied machine learning, deep learning, or reinforcement learning techniques; experience with mobile sensors, sensor networks, instrumentation, or measurement precision; and experience with dynamical systems, vehicle dynamics, system identification, or structural health monitoring. Job #19904 The position is available immediately. International scholars are welcome to apply. In addition to applying online to the MIT website with a CV and cover letter, interested candidates are asked to submit the following via the positions section at 1) a motivation letter (usually up to one page) stating your interest in working with SCL and in particular your interest in the Good Vibrations project, key relevant competencies or area of expertise of the applicant, dates of availability; 2) a CV that includes and highlights relevant projects earlier accomplished and key relevant publications (up to five); and 3) a complete publication list with key relevant publications full text attached. Should also be ready to provide letters of recommendation or contacts of the academic referees upon request. 7/22/21 POSTDOCTORAL ASSOCIATE IN MOBILITY AND INFRASTRUCTURE INFORMATICS, Urban Studies and Planning (DUSP)-Senseable City Lab (SCL), to join the Good Vibrations team for the analysis of complex infrastructure systems such as bridges and roadways. Will perform fundamental and applied research on extracting infrastructure information--such as bridge vibrations--from mobility datasets with the lab's multidisciplinary team and external research and industrial partners; analyze complex infrastructure systems such as bridges and roadways at urban and regional scales using big datasets collected by sensors in moving vehicles; contribute to the design and implementation of mathematical and statistical methods that quantify the performance of infrastructure systems based on individual trip data, determine "collective system intelligence" based on aggregate data, and condition assessment and system state changes over time as indicated by incoming data streams; keep current with relevant state-of-the-art engineering, computer science, mathematics, signal processing, and machine learning methods; participate in research projects with industrial partners; present research at top international workshops and conferences, exhibits, and internal project meetings; and co-author articles for publication in leading peer-reviewed journals and conferences.

Created: 2024-11-02