Job Description 【募集背景】 車載半導体の高機能化・複雑化に伴い、量産テストや不良解析においても、従来の人手・経験依存から、データドリブンかつAI活用型への転換が求められています。 当社では、R-CarやRH850を中核とする車載半導体事業を将来の成長領域と位置づけており、テスト・不良解析技術の高度化が競争力を左右する重要なテーマとなっています。 そこで本ポジションでは、次世代アーキテクチャ（Chiplet／MDP）も見据え、量産現場で実際に使われるテストデータ解析・AI技術を創出するため、体制強化を行います。 【業務内容】 車載向け半導体製品の量産テストおよび不良解析領域において、統計解析手法およびAI技術を活用し、テストデータ解析・不良解析・テスト制御の自動化と高度化を推進するポジションです。 量産現場で蓄積される膨大なテストデータを活用し、品質・コスト・開発スピードの向上に貢献するとともに、ChipletやMDP（Multi‑Die Package）など次世代アーキテクチャに対応したテストデータ解析技術の確立に取り組んでいただきます。 また、技術の体系化および技術継承も重要なミッションの一つです。 【具体的な業務内容】 量産テストデータを活用したデータ解析および不良解析 AI／機械学習技術を用いたテストデータ解析技術の研究・開発 （不良予測、異常検知、解析自動化モデルの開発） テスト効率化および品質向上技術の開発 （アダプティブテスト、テストフィードフォワード／フィードバック技術） 次世代製品（Chiplet／MDP）向けテストデータ解析手法の検討・確立 ※就業場所の変更の範囲、従事すべき業務の変更の範囲については、選考時に詳細をお伝えいたします。 Qualifications 【MUST】 AI／機械学習を含むデータ解析・統計分析の実務経験（5年以上） 半導体テストに関する基礎知識 言語・ツールスキル: Python、Linux 日本語：コミュニケーションレベル 英語：相手の話が聞けるレベル 【WANT】 半導体テストエンジニアとしての実務経験 既存技術やノウハウを体系的に整理・再構成できる力 部門横断でのコミュニケーション力 Chiplet／MDP に関する知識 言語・ツールスキル: Java、SQL、Docker、Git、Jupyter Notebook、FastAPI
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