Residencies at SandboxAQ
The SandboxAQ Residency Program is an excellent, paid opportunity for graduate students or postdocs to work on impactful problems relevant to industry. Residents can work within all of the teams at SandboxAQ on various projects, depending on their skills, interests, and goals. During their time with the program, we encourage residents to contribute to publications, attend conferences, and pursue their professional development. In addition to technical work, community is very important to us. We provide space and events to network and socialize with other participants in the program and the SandboxAQ community as a whole.
Simulation and Optimization (S&O) Team Charter
The S&O team develops AI and quantum solutions for computational science, with a near-term focus on materials design and drug discovery. We seek revolutionary impact on human health, the environment, and the economy. S&O Residents will develop and implement novel physics-based, AI, quantum, and quantum-inspired algorithms for materials design and beyond. Working closely with a focused team of PhDs, MBAs, engineers, and product experts, you will confront hard problems, make business impact, and improve lives.
About the Role
The S&O division is looking for a highly motivated and talented individual to join our multi-disciplinary team of physicists, chemists, and computer scientists to work on applications of machine learning to material science, chemistry, and biology. The focus of this position will be on the research and development of new, innovative approaches to model properties of challenging materials and molecules, using and improving upon the latest state-of-the-art machine learning and deep learning methods.
Responsibilities
- Research and Development
- Conduct research to advance the understanding and application of machine learning techniques in chemistry and structural biology
- Research and develop equivariant machine learning potentials to capture symmetry properties of atomic systems
- Quantum Physics/Chemistry
- Apply expertise from quantum physics/chemistry to inform and guide the development of machine-learning models for atomic systems
- Scientific Computing
- Utilize scientific computing techniques to implement, train, and optimize machine learning models
- Collaborate with interdisciplinary teams to integrate machine learning models into existing computational frameworks
- Code Development
- Write, optimize, and maintain code for machine learning models and simulations
- Collaborate with team members to ensure code reliability, reproducibility, and scalability
- Collaboration and Communication
- Collaborate with researchers within the department and across disciplines.
- Communicate research findings through publications, presentations, and conference participation
Qualifications
- Ph.D. in Physics, Chemistry, Computer Science, or a related field with a focus on one or more of algorithm development for many-body systems, machine learning applications in atomic systems, tensor networks, or similar.
- Expertise in developing neural network-based machine learning potentials. Expertise in equivariant machine learning potentials is a strong plus.
- Strong background in quantum physics and demonstrated experience in applying quantum concepts to machine learning models
- Domain expertise in algorithm development in one or more areas of machine learning, scientific computing, many-body physics, tensor networks
- Proficiency in scientific computing, numerical methods, and programming languages such as Python, Jax, PyTorch
- Proven track record of independent research.
- Excellent communication skills, including the ability to convey complex concepts to both technical and non-technical audiences.
- Strong analytical and problem-solving skills.
Preferred Qualifications
- Experience with transition metal systems
Other Details
- Start date: Year-round, on a rolling basis
- Duration: 1-2 years
