| Position ID: | Texas A&M University-Texas A&M Institute of Data Science-PDSCIML [#18190, R-035655] |
| Position Title: | Postdoctoral Research Associate - Scientific Machine Learning |
| Position Type: | Postdoctoral |
| Position Location: | College Station, Texas 77843, United States [map] |
| Subject Areas: | Data Science / Machine Learning Scientific Computing |
| Appl Deadline: | none (posted 2021/02/23) |
| Position Description: |
| The Texas A&M Institute of Data Science (TAMIDS) has established its Scientific Machine Learning (SciML) Lab as part of a new initiative to develop knowledge, resources, and community around thematic areas of Data Science / Artificial Intelligence / Machine Learning, encompassing research, education, and outreach. TAMIDS supports each thematic Lab through a combination of seed funding for new research, effort from TAMIDS personnel, and support from TAMIDS programs to develop resources for education, training and research. TAMIDS is seeking to recruit a postdoctoral research associate to join the SciML Lab multidisciplinary team, currently comprising six faculty drawn from the Colleges of Science and Engineering, researchers from TAMIDS and Texas A&M High Performance Research Computing, and associated graduate students. Duties: The postdoc will undertake original high-quality research within the multidisciplinary collaborative groups in SciML Lab, produce publications, conference papers and other research outputs, and attend and present of research findings at appropriate conferences and meetings. They will promote the wider adoption of their research results within and beyond SciML Lab, through talks and short courses, developing software packages to implement their research results and providing consultancy on their usage to other researchers, and by contributing to SciML Lab online resources. They will assist with mentoring research students in the group and/or interdisciplinary student team projects. Knowledge and Experience: The successful candidate will have PhD in Science or Engineering, with a disciplinary background in Computer Science, Electrical Engineering, Statistics, Mathematics or a discipline related to Machine Learning and Scientific Computation. They will have strong programming skills and demonstrated expertise in machine learning, together with the ability to work in multidisciplinary teams, good communication skills, and the ability to multi-task and work cooperatively with others. They will preferably have the following knowledge, skills, and abilities: advanced knowledge of contemporary machine learning methods and foundations; experience with high performance computing and numerical methods to solve science and engineering problems; strong programming skills including experience with Keras / TensorFlow / Python / Julia, together with strong interpersonal skills and the ability to cultivate and maintain strong working relationships with people of diverse backgrounds.
Application Procedure: All applications must be submitted following the procedure detailed on the job posting on the TAMU Jobs Worksite, also linked below. Equal Opportunity/Affirmative Action/Veterans/Disability Employer committed to diversity: We seek to hire a candidate who will support the University’s commitment to Inclusion, Diversity, Equity and Accountability (IDEA). We seek to hire a team player who will promote collaboration and cooperation while working within a team. If the description sounds interesting to you, we invite you to apply to be considered for this opportunity to join our team. COVID-19 information: Texas A&M University monitors and updates our recruiting, hiring and onboarding processes to align with local, state and federally mandated health guidelines to keep employees, prospective employees, and visitors as safe as possible. Each department has established both remote and in-person procedures for conducting these processes, which include sanitizing common spaces, a mask policy, and maintaining safe physical distancing to the extent possible. Departments will provide the procedures to be followed at each step of the recruiting process.
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