Internship: Data-based Dynamic Modeling of Vapor Compression Systems
Company: Mitsubishi Electric Research Laboratories
Posted on: February 24, 2021
MERL is seeking a motivated and qualified individual to conduct
research in dynamic modeling of vapor compression systems.
Knowledge of data-based modeling techniques such as neural network
and support vector regression is required. Experience in working
with thermo-fluid systems is preferred. The intern is expected to
collaborate with MERL researchers to build models, develop
algorithms, and prepare manuscripts for scientific publications.
Senior Ph.D. students in applied mathematics, chemical/mechanical
engineering and other related areas are encouraged to apply. The
expected duration of the internship is 3 months and the start date
is flexible. This internship is preferred to be onsite at MERL, but
may be done remotely where you live if the COVID pandemic makes it
Research Areas Multi-Physical Modeling
Contact Hongtao Qiao
Mitsubishi Electric Research Labs, Inc. "MERL" provides equal
employment opportunities (EEO) to all employees and applicants for
employment without regard to race, color, religion, sex, national
origin, age, disability or genetics. In addition to federal law
requirements, MERL complies with applicable state and local laws
governing nondiscrimination in employment in every location in
which the company has facilities. This policy applies to all terms
and conditions of employment, including recruiting, hiring,
placement, promotion, termination, layoff, recall, transfer, leaves
of absence, compensation and training.
MERL expressly prohibits any form of workplace harassment based on
race, color, religion, gender, sexual orientation, gender identity
or expression, national origin, age, genetic information,
disability, or veteran status. Improper interference with the
ability of MERL's employees to perform their job duties may result
in discipline up to and including discharge.
Keywords: Mitsubishi Electric Research Laboratories, Cambridge , Internship: Data-based Dynamic Modeling of Vapor Compression Systems, Other , Cambridge, Massachusetts
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