ASMLAlgorithm intern (Internship)

Location & Duration Sydney Central; 6-12 months Role Overview You will participate in the research and development of human aesthetic enhancement and spatiotemporally consistent editing technologies at Meitu. You will work directly with real, product-scale datasets and state-of-the-art algorithms. Depending on the internship track, your work may include (but is not limited to): ·      Fine-grained and controllable image / video aesthetic enhancement ·      2D / 3D human tracking and 3D reconstruction ·      Regression, reconstruction, and structural constraints of digital human models (e.g., SMPL) This role offers the opportunity to produce both production-ready technical outcomes and high-quality academic research results. It is a research-and-engineering-oriented internship, ideal for candidates with strong interest and capability in 3D vision fundamentals, human visual quality enhancement, video generation models, and 3D human modelling. Key Responsibilities ·      Research and implement algorithms related to depth estimation, multi-view generation, and 2D / 3D tracking with spatiotemporal reconstruction ·      Follow state-of-the-art 3D vision papers and open-source projects; reproduce experiments and adapt methods to practical applications ·      Collaborate with data teams to refine the 3D aesthetic development pipeline, improve data collection and quality evaluation, and establish foundations for high-quality scaling ·      Explore the integration of human structure priors (Skeleton / SMPL / Mesh) with multi-modal cues such as depth, normals, and optical flow in reconstruction and generative models ·      Assist in building data processing, evaluation, and visualization tools (e.g., immersive video aesthetic editing) to support rapid iteration ·      Enable high-quality projection of 3D features into 2D visual outputs, with the goal of producing A-level or above academic publications

About the Team We are a visual R&D team focused on human aesthetic modelling and advanced 3D vision research. Our work spans human image understanding, 3D reconstruction, and intelligent aesthetic enhancement. By combining academic research methodologies with real-world product deployment, we continuously explore new frontiers in AI-driven image/video generation, editing, spatiotemporal consistency, and 3D structural understanding. Location & Duration Sydney Central; 6-12 months Role Overview You will participate in the research and development of human aesthetic enhancement and spatiotemporally consistent editing technologies at Meitu. You will work directly with real, product-scale datasets and state-of-the-art algorithms. Depending on the internship track, your work may include (but is not limited to): ·      Fine-grained and controllable image / video aesthetic enhancement ·      2D / 3D human tracking and 3D reconstruction ·      Regression, reconstruction, and structural constraints of digital human models (e.g., SMPL) This role offers the opportunity to produce both production-ready technical outcomes and high-quality academic research results. It is a research-and-engineering-oriented internship, ideal for candidates with strong interest and capability in 3D vision fundamentals, human visual quality enhancement, video generation models, and 3D human modelling. Key Responsibilities ·      Research and implement algorithms related to depth estimation, multi-view generation, and 2D / 3D tracking with spatiotemporal reconstruction ·      Follow state-of-the-art 3D vision papers and open-source projects; reproduce experiments and adapt methods to practical applications ·      Collaborate with data teams to refine the 3D aesthetic development pipeline, improve data collection and quality evaluation, and establish foundations for high-quality scaling ·      Explore the integration of human structure priors (Skeleton / SMPL / Mesh) with multi-modal cues such as depth, normals, and optical flow in reconstruction and generative models ·      Assist in building data processing, evaluation, and visualization tools (e.g., immersive video aesthetic editing) to support rapid iteration ·      Enable high-quality projection of 3D features into 2D visual outputs, with the goal of producing A-level or above academic publications

1. 版图布局算法研究：参与版图自动布局的算法研究、数据处理及可视化工程工作； 2. 前沿技术调研与分享：负责开源项目和公开论文的收集、研读及分享，推动团队技术积累； 3. 团队协同支持：完成其他团队协同所需的工作事项，保障项目顺利推进。

1. 多模态大模型研发：参与芯片领域多模态大模型（MLLM）的研发，探索大模型在EDA工具、电路设计、版图优化等场景的应用； 2. 智能体机制构建：协助构建芯片设计领域Agent的长短期记忆机制和自进化能力； 3. 工具集成与优化：参与大模型在EDA工具中的集成与优化，探索AI辅助芯片设计的新方法； 4. 前沿跟踪与论文产出：跟踪学术界和工业界最新进展，复现和改进前沿算法，撰写高质量学术论文，推动在NeurIPS、ICLR、ICML、CVPR、ACL、ICCAD等顶级会议投稿； 5. 业务落地协作：与EDA业务方紧密合作，推动MLLM在实际芯片设计流程中的落地应用。