Recruit Detail
Find out more about the work we do, the experience and skills we can bring to the table, and our terms and conditions.
Company Name
Preferred Networks, Inc.
Job Type
Material Discovery Researcher / Researcher (Materials)
Work Detail
We are recruiting researchers for the materials discovery division. Numerical techniques in chemistry and materials science have made remarkable progress, and in recent years, computational development of new materials has been attracting attention. PFN has developed Matlantis™, a general-purpose atomic-level simulator, in collaboration with ENEOS Corporation, in order to develop innovative materials by highly integrating computational science and materials development. PFN is working on further technological development of Matlantis and physical simulation technology based on deep learning, with the goal of realizing material discovery and property prediction that far exceeds the conventional limits of application. We are looking for people who want to develop unprecedented simulation technology and material proposal methods by combining computational science and deep learning, and who want to develop new materials together with our customers by doing so. We welcome applications from those who have knowledge and experience in related fields, as well as those who share this ambitious goal and are enthusiastic about it. The expected responsibilities include the following. The scope of work may be adjusted flexibly depending on your skills, experience, and interests. **Responsibilities** Research and development related to Matlantis - Advance the technical development of Matlantis based on the latest research trends and customer needs. Develop new features and improve the performance of Matlantis by applying expertise in deep learning and materials science - Generalize technologies developed through collaborative or internal research and implement them as product features or solution components - Promote technology transfer from research to product development, taking a product-owner-like perspective where appropriate Collaborative materials development with partner companies - Clarify customer challenges and propose solutions by leveraging PFN’s technology assets and domain expertise - Contribute to solving customer challenges through the development of innovative technologies for materials discovery - Communicate effectively with customers, including regular progress updates, and feedback incorporate into research plans - Create opportunities for collaborative and commissioned research through technical presentations, proposals, and external communication Research, development, and productization of new materials discovery methods - Continuously follow the latest research and publications, and develop innovative technologies that contribute to materials discovery with enthusiasm - Build research assets that can serve as the foundation for future collaborative projects and product features Development of research infrastructure to accelerate R&D activities - Transform research outcomes into reusable assets, including code, datasets, validation methods, workflows, and technical documentation - Collaborate with internal teams working on computational infrastructure, generative AI, foundation models, and related technologies to build technical assets **Related Links** [Materials Science Blog](https://tech.preferred.jp/blog/area/chemoinformatics-and-materials-science/) [Publications](https://tech.preferred.jp/publications/area/chemoinformatics-and-materials-science/) [Matlantis Website](https://matlantis.com/ja/) [Matlantis Calculation Examples](https://matlantis.com/ja/cases#calculation) [Matlantis [Paper](https://www.nature.com/articles/s41467-022-30687-9) PFN is seeking researchers to lead its materials business. The development of numerical computation technology in chemistry and materials science has been remarkable, and in recent years, computer-aided materials development has been attracting attention. PFN, aiming to achieve innovative materials development by highly integrating computational science and materials development, has jointly developed the general-purpose atomic-level simulator Matlantis™ with ENEOS Corporation. PFN is working on further technological development of Matlantis and the development of physical simulation technology based on deep learning, with the goal of achieving materials discovery and property prediction that far exceeds the limitations of conventional applications. We are looking for individuals who want to develop unprecedented simulation technology and materials proposal methods by combining computational science and deep learning, and who want to develop new materials together with our customers. We welcome applications from those with knowledge and experience in related fields, as well as those who share this ambitious goal and possess enthusiasm. The following are envisioned as specific job duties. Please note that the scope of responsibilities will be flexibly adjusted based on your experience, skills, and interests. **Job Duties** Research and Development related to Matlantis - Work on further technological development of Matlantis based on the latest research trends and customer requests. Leveraging expertise in deep learning and materials science, we will drive the development of additional features and performance improvements for Matlantis. - Generalize technologies obtained through collaborative and in-house research and implement them as product features. - Promote technology transfer of research results from a perspective close to that of the product owner, as needed. Joint development of materials with other companies: - Clarify customer challenges and consider and propose solutions based on PFN's technological assets and expertise. - Contribute to solving customer challenges through technological development toward innovative materials development. - Communicate appropriately with customers, including regular progress reports, and reflect feedback in research plans. - Contribute to creating opportunities for collaborative and contract research through technical presentations, proposal activities, and external dissemination. Research and development and productization of new materials development methods: - Continuously keep up with the latest research and papers, and enthusiastically devise and develop innovative technologies that contribute to materials development. - Build research assets that will serve as the foundation for future collaborative research and product features. Building a research infrastructure to promote research activities: - Organize research results into reusable formats such as code, datasets, verification methods, workflows, and technical documentation. - We will collaborate with other departments within the company involved in computational infrastructure, generative AI, and foundational models to build technological assets that accelerate research and development. **Related Links** [Materials Science Blog](https://tech.preferred.jp/blog/area/chemoinformatics-and-materials-science/) [Publications](https://tech.preferred.jp/publications/area/chemoinformatics-and-materials-science/) [Matlantis HP](https://matlantis.com/ja/) [Matlantis Calculation Examples](https://matlantis.com/ja/cases#calculation) [Matlantis Papers](https://www.nature.com/articles/s41467-022-30687-9)
Ideal Profile
Qualifications (Required) - Knowledge and Motivation - Basic knowledge or interest in either physics, chemistry, or materials science - Basic knowledge of machine learning and deep learning - Ability to follow and implement the latest paper trends - In-depth knowledge and experience in your field of expertise. - Willingness to constantly learn new domain knowledge - Ability to actively engage in discussions with engineers from different fields and companies. - Experience - Experience solving problems in a team environment - Experience solving problems using computer science knowledge - Always keeping up with cutting-edge technology with the goal of becoming familiar with all disciplines of computer science - Software development experience in Python, Go, C, C++, Java, or similar languages - Ability to write programs with an understanding of computer architecture and an awareness of software execution efficiency and computational load - Knowledge and Motivation - Basic knowledge or interest in either physics, chemistry, or materials science - Basic knowledge of machine learning and deep learning - Ability to follow and implement the latest paper trends - Deep knowledge and experience in your area of expertise - A constant willingness to learn new domains - Ability to actively discuss with engineers from different fields and companies - Experience - Experience in team problem-solving - Experience in problem-solving utilizing computer science knowledge - A desire to become proficient in all areas of computer science and to constantly pursue cutting-edge technologies - Software development experience (Python, Go, C, C++, Java, etc.) - Understanding computer architecture and the ability to create programs that are mindful of software execution efficiency and computational complexity Preferred Qualifications *You do not need to have all of the skills listed below. We expect you to have excellent expertise in any of them. *You do not need to have all of the skills listed below. We expect you to have excellent expertise in any of them. - Compatibility with the ongoing projects at PFN Materials Group. Specifically, knowledge and experience with various methods in the fields of atomic simulation, interatomic potentials, electronic structure calculations, etc., and their integration with machine learning. - Notwithstanding the above, expertise and motivation to broaden the scope of activities of PFN Materials Group. Specifically, integration with the experimental science side, such as measurements and macro-scale phenomena, application of new simulation and machine learning methods, expertise in specific materials, and experience in research and development in the field of Materials Informatics. - Experience in research and development, writing papers, or conference presentations on the integration of physical simulation and machine learning. - Knowledge of, development experience with, or experience implementing similar computational chemistry software and ancillary tools. For example, molecular dynamics software (Atomic Simulation Environment (ASE), LAMMPS, OpenMM, etc.), electronic structure calculation software (VASP, GAUSSIAN, ORCA). - Experience in software development such as application development, library development, or Unix/Linux server operations. - Machine learning and deep learning related research and development experience - Interest in and experience with computational implementation efficiency and optimization related to physical simulation - Experience in data analysis skills and application of computer science knowledge. Experience and achievements in data analysis techniques using machine learning and statistical tools, programming competitions, game AI contests, data analysis Contests, etc. Knowledge, implementation, and experience using mathematical optimization, search, and numerical analysis. - Compatibility with various projects currently underway in the Materials Division at PFN. Specifically, knowledge and experience in various methods in fields such as atomic simulation, interatomic potential, and electronic state calculation, as well as the integration of these with machine learning. - Regardless of the above, expertise and motivation to expand the scope of activities of the Materials Division at PFN. Specifically, integration with experimental science such as measurements and macro-scale phenomena, application of new simulation and machine learning methods, expertise in specific materials, and research and development experience in the field of materials informatics. - Experience in research and development, paper writing, and conference presentations related to the integration of physical simulation and machine learning. - Knowledge of computational chemistry software and auxiliary tools, development experience, or implementation experience of similar software. For example, molecular dynamics software (Atomic Simulation Environment (ASE), LAMMPS, OpenMM, etc.), electronic state calculation software (VASP, GAUSSIAN, ORCA, etc.) - Practical experience in software development. For example, application development, library development, and Unix/Linux server operation experience. - Research and development experience related to machine learning and deep learning - Interest in and implementation experience with computational efficiency improvements and optimizations for physical simulations - Achievements and experience in programming competitions, game AI competitions, and data analysis competitions (such as Kaggle). Knowledge, implementation, and usage experience in mathematical optimization, search, and numerical analysis.
Work Location
Otemachi Bldg., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004 Remote work system available (limited to work in Japan)
Phd. Stating Salary
Compensation will be determined based on experience, performance, abilities, and contributions, in accordance with company regulations.