Duties: Contribute to the design and analysis of algorithms for mathematical optimization and machine learning, and to the modeling for complex design, decision, and inference problems; Collaborate in the development and application of software instantiations of numerical algorithms; Document work and results in the form of journal papers and conference proceedings, and present work and results at scientific meetings and conferences; Bring creative and innovative perspectives by proposing algorithmic approaches, models, and solutions; Engage and interact with multidisciplinary science and engineering teams
Requirements: Ph.D. degree in applied mathematics, computer/computational science, data science, operations research, or a related field is required; Fundamental knowledge of mathematical optimization, statistics, or machine learning

Duties: Develop theory and optimization techniques for tackling noise and missing data for improved subtomogram resolution; Develop algorithms for automated marker-less alignment of X-ray and electron tomography data; Develop new data-driven methods that leverage physics-informed machine learning for reconstructing non-rigid deformations and generative modeling of conformational heterogeneity in electron tomography; Apply these new algorithms to enable high-resolution 3D reconstructions of biomolecules from cellular tomographic data; Publish scientific papers in high-impact journals and present findings at seminars and conferences; Maintain documentation of theory, derivations, and results
Requirements: A recent Ph.D. (within the last 1-2 years) in Applied Mathematics, Computational Biophysics/Physics, Computer Science, Data Science or a related discipline; Experience developing numerical methods for solving inverse problems in imaging including but not limited to phase retrieval, iterative reconstruction for tomography, and regularization techniques; Experience with generative models, variational inference, and physics informed machine learning; Strong background in scientific computing including coding experience in C++, Fortran, and Python; Knowledge of physics and mathematics of X-ray and electron microscopy

Duties: As part of the team contribute to the development of efficient algorithms for quantum simulations; Contribute to the implementation of algorithms in open-source software tools for quantum computing; Contribute to the advancement of software tools developing optimal quantum source code; As part of an interdisciplinary team contribute to the design and execution simulations on physical hardware; Publish results in peer reviewed journals and conferences
Requirements: PhD degree in chemistry, physics, applied mathematics or a relevant field is required; Demonstrated experience in quantum computing and related algorithm and software development is required; Strong programming skills in Python

Duties: Optimize and deploy state-of-the-art deep learning models for protein sequences and structures; Develop efficient, high-quality code and data pipelines; Implement, analyze, and interpret multiple computational approaches and present results to colleagues in regular update meetings; Establish automated processes to continuously evaluate and improve our protein design methodology; Work within a collaborative, fast-paced, interdisciplinary team across biology and machine learning to help shape the scientific and strategic vision of the company
Requirements: MS or PhD in Computer Science, Machine Learning, Natural Language Processing, Applied Math, Computational Biology, Statistics, or a related field; 2+ years of industry experience in machine learning infrastructure, pipeline building, distributed training, and deployment; Demonstrated ability in re-implementation of multiple state-of-the-art models from research for comparative analysis

Duties: Design and develop state-of-the-art deep learning methods for protein sequence, structure, and function prediction and apply them to protein design; Collaborate with Biology team to design and characterize novel designed biomolecules; Implement, analyze, and interpret multiple computational approaches and present results to colleagues in regular update meetings; Work within a collaborative, fast-paced, interdisciplinary team across biology and machine learning to help shape the scientific and strategic vision of the company
Requirements: PhD in Computer Science, Machine Learning, Natural Language Processing, Applied Math, Computational Biology, Statistics, or a related field; Experience with conceiving of, implementing, and developing novel machine learning techniques; Publications at major machine learning conferences (NeurIPS, ICML, ICLR) or scientific journals (Nature, Science, Nature Biotech, Nature Methods, PNAS); Experience with modern deep learning frameworks such as Pytorch or Jax