Success Story: EB2 NIW Approval for a Postdoctoral Fellow in the field of Mechanical Engineering in a month
Client’s Testimonial:
I am very grateful for your help, which cannot be put in words. Thank you so much. You did an amazing job and getting the approval in the short period of time is an evidence of your expertise. I will be more than happy to recommend you to my friends. Many many thanks for everything.
On May 6th, 2013, we received another EB2 NIW (National Interest Waiver) Approval for a Postdoctoral Fellow in the field of Mechanical Engineering (Approval Notice)
General Field: Mechanical Engineering Position at the Time of Case Filing: Postdoc Fellow National Origin: Iran Service Center: Texas (TSC) State Residing at the Time of Filing: Connecticut Approval Notice Date: May 6, 2013 Processing Time: 1 month
Case Summary:
The average processing time for I-140 cases is 4-6 months. But on many occasions, we have cases approved far faster than the average processing time.
This client is a Postdoctoral Fellow from Iran in the field of mechanical engineering. His work has specifically focused on the area of clean energy conversion fuel cell technology development by means of modeling, Computational Fluid Dynamics, as well as thermo-fluid system design, such as heat exchangers. He wished to seek employment in the field of mechanical engineering. In order to ensure the success of this case, we submitted extensive documentation of the client’s highly significant contributions to the field of mechanical engineering, including his authorship of eight peer-reviewed scientific articles published in leading journals (seven of which are first-authored) and 15 conference proceedings. Additionally, our firm drafted and submitted several recommendation letters from experts in our client’s shared field. One of the independent recommenders noted “Another example of [the Petitioner’s] revolutionary achievements is his novel work introducing an Artificial Neural Network (ANN) to predict, model, and analyze a polymer electrolyte fuel cell catalyst layer’s performance. As he showed in his publication on CL mathematical modeling, there are numerous nano-microstructural parameters that affect the CL performance. Taking into consideration the effects of all parameters for the CL performance enhancement is time consuming work. So, there were questions on how to describe CL performance in a simpler and faster way and to what level the CL is affected by dependent structural parameters. He solved these inquiries by designing an artificial neural network for CL performance prediction and was the first researcher ever to do so.” With the strong petition letter and recommendation letters we prepared, his NIW case was approved in just one month.