Job Search

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

Eisai Co., Ltd.

Job Type

computational science researcher

Work Detail

* Utilizing computational science and technology to search for hit compounds that act on target molecules and to advance drug discovery projects by improving the efficiency of developing lead compounds from hits. * Development and application of a unique SBDD method combining experimental data with internal and external computational science and technology (including molecular dynamics simulations and machine learning). * Collaboration with experimental researchers to develop and apply methods that contribute to the realization of a rational drug discovery process for undruggable target molecules and various modalities (peptides, antibodies, functional molecules, etc.).

Ideal Profile

In addition to sharing our corporate philosophy of hhc (Human Health Care), we are seeking individuals who possess the following four qualities: * Sense of Ownership/Situational Awareness Individuals who have persevered through difficulties without giving up, and who have consistently kept track of relevant situations and environmental changes. * Proactiveness Individuals who have taken initiative and achieved results exceeding expectations, and who have persevered through difficulties without giving up. * Logical Thinking Individuals who have not been constrained by the status quo, but have offered insightful observations and questions, and who have proposed new ideas through ingenuity. * Communication Skills Individuals who have listened attentively to others, understood their true intentions, and responded accordingly, and who can clearly and concisely communicate their own thoughts.

What we especially look for in a PhD or postdoctoral fellow

As doctoral students, you are undoubtedly going through various trials and errors in your research activities to achieve the desired results. In addition to the four elements mentioned above, we especially hope that doctoral students will contribute to drug discovery and healthcare with the spirit of GRIT: the ability to not give up after just one or two attempts, and the ability to persevere until the end even if they are not initially understood by those around them.

Work Location

Ibaraki Prefecture, Tokyo, Hyogo Prefecture

Phd. Stating Salary

436,800 yen

Similar Recruits

Preferred Networks, Inc.

Job Type
Machine Learning, Optimization, and Data Science Engineer

We are recruiting engineers in the fields of machine learning, optimization, and data science year-round. We collaborate with client companies across diverse industrial sectors to solve challenges and realize PFN's vision of "making the real world computable" through research and development and implementation using our own supercomputers. We aim to provide high-value solutions that only PFN can offer, by learning specialized knowledge in each business domain and utilizing a broad range of computer science knowledge and implementation skills. We seek individuals who can actively communicate with people from diverse backgrounds, both inside and outside the company, and who can continuously learn new knowledge in line with PFN's "Learn or Die" values, thereby creating new value. The following are examples of work; however, actual work is not limited to these. The projects and tasks you will actually be responsible for after joining the company will be determined based on your specialized knowledge and experience. **Examples of Work** As an engineer, you will manage and promote projects for problem-solving, including new projects. - Launching new projects, conducting customer interviews, defining requirements, engineering, and reporting. - Observation and analysis of data related to existing equipment and processes, as well as their operation and management. - Defining tasks suitable for resolution using machine learning techniques and satisfying customer needs. - Establishing and implementing evaluation methods for machine learning model operation and application behavior based on actual equipment, data, or simulations, and conducting technical verification using these methods. - Explaining machine learning model operation and control behavior to customers and users, and developing, implementing, and verifying technical methods to enhance explainability (such as visualization). - Providing technical advice to engineers at client companies. We provide problem-solving services such as time series forecasting, mathematical optimization, and inverse problem analysis, depending on the problem domain and the client's business domain. - Construction and verification of machine learning models for prediction or anomaly detection of time-series data related to target equipment and processes. - Proposal, implementation, and experimentation of solutions to real-world combinatorial optimization problems from a multifaceted perspective including heuristics, mathematical programming, and machine learning. - Inverse problem analysis and estimation of the internal state and structure of measured objects using real-world remote sensing data. - Domain adaptation of simulation data and real-world data. We develop technologies to control large-scale production facilities such as chemical plants in operation, as well as industrial machinery and robotic equipment, to enable safer and more optimal operation. - Construction of machine learning models that enable optimal control while guaranteeing safety. - Construction of machine learning models that detect and predict equipment anomalies using time-series operating data. We develop technologies for efficient manufacturing, utilizing simulations and other methods, for manufacturing processes and equipment. - Formulating problems using 2D/3D particle simulations and applying them to machine learning - Development of continuous and discrete optimization algorithms for exploring optimal manufacturing processes - Construction and operation of a system that performs optimization processing using developed algorithms and machine learning models in conjunction with our own cluster We propose data analysis and problem-solving solutions that lead to improved profitability and customer satisfaction in the retail industry. We develop methods that contribute to the restructuring of operations and improvement of operational quality in the retail industry. - Problem setting and hypothesis building through observation of store operations and discussions with stakeholders - Leading the construction of data collection and analysis infrastructure in collaboration with other internal teams and client companies - Development of image and POS data analysis and optimization methods for the purpose of sales promotion, avoiding lost opportunities, and inventory optimization - Verification of the effectiveness of development methods in collaboration with client companies