ORIgen , Inc.
Origen.AI , an artificial intelligence startup focusing on the energy sector, is looking for one or several (senior) Deep Learning Researchers specializing in physics-informed neural networks (PINNs) to join our team in our offices in Hoboken, New Jersey. Origen.AI has developed cutting edge technology in this sector, solving significant challenges of differential equations for PINNs, such as Buckley Leverett. Currently the OriGen team is expanding PINNs for real problems such as fluid dynamics in porous media or CO2 sequestration. We are creating a passionate and engaging culture that combines cutting-edge research and product-led engineering to provide a supportive balance of structure and flexibility. We are looking for colleagues passionate about bringing cutting-edge AI to industrial problems. We are willing to hire both on the senior level and at a more intermediate level, depending on the level of experience of the best candidates. Origen.AI is a proud Equal Opportunity Employer. We consider all qualified applicants regardless of race, gender, age, disability, religion, sexual orientation, national origin, veteran status, or any other characteristic protected by law. The Job role Includes: Inventing and refining deep learning methods for predicting geophysical dynamics, with a current focus heavily towards particular oil reservoir dynamics. Interfacing with the software engineering team who are continuously enhancing our PROTEUS platform for our customers in the energy sector. Working closely with a research with a high impact in applied deep learning. We Offer: An intellectually challenging set of problems An opportunity to perform cutting edge AI research on problems of critical importance for our future Colleagues with deep knowledge of the method and domain Excellent opportunities to grow with the company as we scale up Stock options Opportunities to publish and patent Leading research in one of the most promising startup in USA What we Expect from You: Strong knowledge of deep learning methods Experience with physics-informed neural networks problem Experience creating deep neural network with parallel GPUs Experience 5+ in Python, C++, Experience 5+ with deep learning libraries such as pytorch or tensorflow Published papers within deep learning, or machine learning generally Published papers within deep learning in applied areas in physics-informed neural networks Ability to communicate your ideas clearly and work in teams Familiarity with engineering physics or mechanical engineering Design, implement and evaluate models, agents and software prototypes of perceptual processing Report and present research findings and developments (including status and results) clearly and efficiently, both internally and externally, verbally and in writing Suggest and engage in team collaborations to meet ambitious research goals Qualifications: Minimum Qualifications: PhD or equivalent practical experience Preferred Qualifications 5+ years of experience after PhD PhD in machine learning, neuroscience, or computer science Relevant publications in physics-informed neural networks Experience with coding GPUs (CUDA) Relevant experience to the position, such as post doctoral roles, a proven track record of publications, or deep neural network architectures A real passion for AI Excellent communication skills in English How to Apply Does this role sound like a good fit? Please apply below: Complete the survey in the application link Attach your most recent CV in PDF format Send along links that best showcase the relevant things you've built and done Note: If your application is successful and you receive an offer of employment, the offer will be conditional upon the results of a background check conducted by a third party on our behalf. #J-18808-Ljbffr