[ad_1]
Here at Amazon, we embrace our differences. We are committed to furthering our culture of diversity and inclusion of our teams within the organization. How do you get items to customers quickly, cost-effectively, and—most importantly—safely, in less than an hour? And how do you do it in a way that can scale? Our teams of hundreds of scientists, engineers, aerospace professionals, and futurists have been working hard to do just that! We are delivering to customers, and are excited for what’s to come. Check out more information about Prime Air on the About Amazon blog (https://www.aboutamazon.com/news/transportation/amazon-prime-air-delivery-drone-reveal-photos). If you are seeking an iterative environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide benefits to customers, Prime Air is the place for you. Come work on the Amazon Prime Air Team! Prime Air is seeking an experienced Research Scientist in the Flight Sciences High-Fidelity Methods (HFM) team within Flight Sciences, you will develop and verify aerodynamics models used for engineering analyses and vehicle simulation. These models are the backbone of every flight simulation performed within Prime Air and are a critical element in the aircraft design, verification and certification process. These models are used to predict many attributes of the vehicle performance including range, maneuverability, tracking error, and aircraft stability. They are a key input to design decisions, vehicle component sizing and flight software algorithm development. The accuracy and reliability of these flight model are critical to the success of Prime Air. For this role we are looking for a scientist to develop surrogate or machine learning models to represent the complex aerodynamic behavior of our drones. This scientist will develop techniques to validate these models using flight testing, quantify the model uncertainty, and assess the impact of this uncertainty on downstream engineering analyses. Key job responsibilities A Research Scientist in this role is responsible for owning the development, deployment, verification, and maintenance of models from end-to-end. This includes the initial gathering of the downstream customer needs, identifying the most suitable modelling approach, coordinating the generation of input data, training models, developing and maintaining software interfaces, and verifying the model accuracy. A Research Scientist in this role is responsible for determining the most suitable modeling approach for a given physical phenomena. They need to possess knowledge of various machine learning techniques, and their respective advantages and limitations. They will need to have a detailed understanding of the types of physics to be modelled including vehicle aerodynamics, multibody dynamics, and atmosphere physics. This role is responsible for designing experiments for generating data used to train and verify surrogate models. They need to have a basic understanding of the methods used to generate high-fidelity aerodynamics predictions including CFD, wind tunnel testing, and flight testing. They will be responsible for validating the models by leveraging uncertainty quantification, system identification, and statical analyses. Export Control License This position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on Amazon’s ability to apply for and obtain an export control license on your behalf. A day in the life A Research Scientist in the High-Fidelity Methods (HFM) team will have the opportunity to work on a wide variety of tasks. The ideal candidate should be adaptable and thrive in an everchanging environment. Depending on the phase of model or vehicle development, a typical day might consist of reading research papers on machine learning techniques, developing test plans for wind tunnel testing, writing code to train and verify models, reviewing flight test results, or writing documentation. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
[ad_2]
Source link
