Preferred Networks
2026 New Graduates - Drug Discovery Researcher/Engineer
2025新卒リサーチャー・エンジニア(創薬)
Tags: Full-time, 0~1 YOE, Business Japanese, Remote
Remote, Japan (Remote) / Otemachi, Chiyoda-ku, Tokyo, Japan・Fetched 30 days ago
Job Description
Job Description / 職務内容
Preferred Networks (PFN)のDrug Discovery領域を担うリサーチャー/エンジニアを募集します。創薬分野における計算科学の発展はめざましく、創薬標的となる生体内高分子の同定やその構造・機能の予測、医薬品候補分子の設計・探索・最適化(薬理活性・毒性・動態予測)などの分野において実用化が進んでいます。PFNのDrug Discoveryチームでは最先端技術と創薬分野におけるドメイン知識を高度に融合して、多岐にわたる創薬現場の課題解決に取り組んでいます。現在、PFNでは、創薬現場において有用な新規技術の開発に興味があり、製薬企業と協力して新しい医薬品の開発に携わりたいという熱意のある方々を募集しています。**ミッション**- 最先端の計算科学を活用し、革新的医薬品の創出を支援することで、人々の健康に貢献する**業務内容**- 創薬分野における新規技術の研究開発 - Requirementsに記載の分野を中心に、様々な分野を担当していただきます- 他機関との共同研究- 最新の研究論文の調査および習得- 他チームとの連携**参考**- これまでにPFNが行った研究開発の一部 - Drug Discovery HP: https://projects.preferred.jp/drug-discovery/ - 技術ブログ: https://tech.preferred.jp/ja/tag/drug-discovery/- 弊社事業と関連する計算科学技術のキーワード (募集職種はRequirementsをご確認ください) - Neural Network Potential - Protein Folding, Structure-aware prediction - ADMET予測 - AIによる分子設計 - 計算化学・分子シミュレーション - 分子ドッキング - 分子動力学法(拡張サンプリングMD、Free Energy Perturbationなど) - Structure Based Drug Design (SBDD) - Ligand Based Drug Design (LBDD) - ケモインフォマティクスWe are recruiting researchers and engineers in the field of drug discovery.The advancements of computational science in drug discovery have been remarkable, leading to practical applications in the identification of biomolecular drug targets, prediction of their structures and functions, and the design of drug candidate molecules (predicting pharmacological activity, toxicity, and pharmacokinetics). PFN's drug discovery team is tackling a wide range of challenges in drug discovery by integrating cutting-edge technologies with domain knowledge in the field.Now, PFN welcomes applications from those who are enthusiastic about developing novel technologies useful in drug discovery and collaborating with pharmaceutical companies to develop new medicines.**Mission**- Contribute to human health by facilitating the creation of innovative medicines using cutting-edge computational science.**Responsibilities**- Research and develop new technologies in the field of drug discovery - Primarily responsible for the areas specified in the Requirements, but may also handle a variety of other areas- Conduct collaborative research with pharmaceutical companies- Survey the latest research papers- Collaborate with other teams**Additional Information**- Introduction to our research - Drug Discovery HP: https://projects.preferred.jp/drug-discovery/ - Technical Blog: https://tech.preferred.jp/ja/tag/drug-discovery/- Keywords related to our activities (see Requirements for open positions) - Neural Network Potential - Protein Folding, Structure-aware prediction - ADMET Prediction - AI-driven Molecular Design - Computational Chemistry, Molecular Simulation - Molecular Docking - Molecular Dynamics (e.g., Enhanced Sampling MD, Free Energy Perturbation) - Structure-Based Drug Design (SBDD) - Ligand-Based Drug Design (LBDD) - Chemoinformatics
Qualifications / 応募資格(必須)
- 専門知識 (いずれか1つ以上) - Neural Network Potential (NNP)開発に関する深い知識や経験 - Protein folding / Structure-aware predictionに関する深い知識や経験- 機械学習・深層学習(AI)分野における深い知識や経験- ソフトウェア開発 (Python 等)- Unix/Linuxサーバーの使用経験- In-depth knowledge and experience in one or more of the following areas - the development of Neural Network Potentials - the development of Protein folding or Structure-aware prediction- In-depth knowledge and experience in machine learning and deep learning (AI)- Software development experience (e.g., Python)- Experience with Unix/Linux systems
Preferred Qualifications / 応募資格(歓迎)
- 量子化学計算に関する深い知識や経験 (NNP開発の場合) - とくにQM/MM- NNPを活用した創薬分野における応用の知識や経験 (NNP開発の場合)- In-depth knowledge and experience in quantum chemistry (for NNP development) - especially for QM/MM calculations- Knowledge and experience in the application of Neural Network Potentials (NNP) in drug discovery (for NNP development)
Required documents in addition to a resume
- 論文リスト (もしあれば)- 研究概要 (A4用紙2枚以下)- Paper list (if available)- Research summary (up to 2 pages of A4 paper)
Salary /賃金
経験、業績、能力、貢献に応じて、当社規定により優遇Experience, performance, skills, contribution are taken into consideration.
Location / 勤務地
[Otemachi Bldg., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004 リモート勤務制度あり (日本国内に限る) / Remote work system available (limited to work in Japan)]
Preferred Networks (PFN)のDrug Discovery領域を担うリサーチャー/エンジニアを募集します。創薬分野における計算科学の発展はめざましく、創薬標的となる生体内高分子の同定やその構造・機能の予測、医薬品候補分子の設計・探索・最適化(薬理活性・毒性・動態予測)などの分野において実用化が進んでいます。PFNのDrug Discoveryチームでは最先端技術と創薬分野におけるドメイン知識を高度に融合して、多岐にわたる創薬現場の課題解決に取り組んでいます。現在、PFNでは、創薬現場において有用な新規技術の開発に興味があり、製薬企業と協力して新しい医薬品の開発に携わりたいという熱意のある方々を募集しています。**ミッション**- 最先端の計算科学を活用し、革新的医薬品の創出を支援することで、人々の健康に貢献する**業務内容**- 創薬分野における新規技術の研究開発 - Requirementsに記載の分野を中心に、様々な分野を担当していただきます- 他機関との共同研究- 最新の研究論文の調査および習得- 他チームとの連携**参考**- これまでにPFNが行った研究開発の一部 - Drug Discovery HP: https://projects.preferred.jp/drug-discovery/ - 技術ブログ: https://tech.preferred.jp/ja/tag/drug-discovery/- 弊社事業と関連する計算科学技術のキーワード (募集職種はRequirementsをご確認ください) - Neural Network Potential - Protein Folding, Structure-aware prediction - ADMET予測 - AIによる分子設計 - 計算化学・分子シミュレーション - 分子ドッキング - 分子動力学法(拡張サンプリングMD、Free Energy Perturbationなど) - Structure Based Drug Design (SBDD) - Ligand Based Drug Design (LBDD) - ケモインフォマティクスWe are recruiting researchers and engineers in the field of drug discovery.The advancements of computational science in drug discovery have been remarkable, leading to practical applications in the identification of biomolecular drug targets, prediction of their structures and functions, and the design of drug candidate molecules (predicting pharmacological activity, toxicity, and pharmacokinetics). PFN's drug discovery team is tackling a wide range of challenges in drug discovery by integrating cutting-edge technologies with domain knowledge in the field.Now, PFN welcomes applications from those who are enthusiastic about developing novel technologies useful in drug discovery and collaborating with pharmaceutical companies to develop new medicines.**Mission**- Contribute to human health by facilitating the creation of innovative medicines using cutting-edge computational science.**Responsibilities**- Research and develop new technologies in the field of drug discovery - Primarily responsible for the areas specified in the Requirements, but may also handle a variety of other areas- Conduct collaborative research with pharmaceutical companies- Survey the latest research papers- Collaborate with other teams**Additional Information**- Introduction to our research - Drug Discovery HP: https://projects.preferred.jp/drug-discovery/ - Technical Blog: https://tech.preferred.jp/ja/tag/drug-discovery/- Keywords related to our activities (see Requirements for open positions) - Neural Network Potential - Protein Folding, Structure-aware prediction - ADMET Prediction - AI-driven Molecular Design - Computational Chemistry, Molecular Simulation - Molecular Docking - Molecular Dynamics (e.g., Enhanced Sampling MD, Free Energy Perturbation) - Structure-Based Drug Design (SBDD) - Ligand-Based Drug Design (LBDD) - Chemoinformatics
Qualifications / 応募資格(必須)
- 専門知識 (いずれか1つ以上) - Neural Network Potential (NNP)開発に関する深い知識や経験 - Protein folding / Structure-aware predictionに関する深い知識や経験- 機械学習・深層学習(AI)分野における深い知識や経験- ソフトウェア開発 (Python 等)- Unix/Linuxサーバーの使用経験- In-depth knowledge and experience in one or more of the following areas - the development of Neural Network Potentials - the development of Protein folding or Structure-aware prediction- In-depth knowledge and experience in machine learning and deep learning (AI)- Software development experience (e.g., Python)- Experience with Unix/Linux systems
Preferred Qualifications / 応募資格(歓迎)
- 量子化学計算に関する深い知識や経験 (NNP開発の場合) - とくにQM/MM- NNPを活用した創薬分野における応用の知識や経験 (NNP開発の場合)- In-depth knowledge and experience in quantum chemistry (for NNP development) - especially for QM/MM calculations- Knowledge and experience in the application of Neural Network Potentials (NNP) in drug discovery (for NNP development)
Required documents in addition to a resume
- 論文リスト (もしあれば)- 研究概要 (A4用紙2枚以下)- Paper list (if available)- Research summary (up to 2 pages of A4 paper)
Salary /賃金
経験、業績、能力、貢献に応じて、当社規定により優遇Experience, performance, skills, contribution are taken into consideration.
Location / 勤務地
[Otemachi Bldg., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004 リモート勤務制度あり (日本国内に限る) / Remote work system available (limited to work in Japan)]