博士研究生岗位（男女均可，F/M）：计算存储及其在科学计算中的应用

针对科学计算与人工智能领域的计算存储研究博士研究生岗位，需具备扎实的编程及分布式系统知识。该岗位提供全额支持，并附带每月固定薪资。

Assignment: Context and Overview: Computational storage [15, 16] is a promising technology to improve the efficiency and performance of various workloads, for example in scientific computing, machine learning (ML), and artificial intelligence (AI). As the volume of generated data continues to grow exponentially [5,9] conventional compute and storage architectures are increasingly constrained by large data movements between storage/memory and compute resources. Computational storage can eliminate many of these data movements by co-locating compute capabilities along with storage locations allowing to offload the typically much smaller (sub)programs [1, 12, 14]. While the concept is well established in literature, computational storage devices are not widely commercially available or deployed in data centers today. A key challenge is that computational storage encourages domain-specialization for highest efficiency while economic factors encourage commoditization of products catering to broad markets. A second challenge is that both for legacy applications as well as emerging applications such as ML/AI it remains an open research question how to program and orchestrate across distributed platforms with computational storage capabilities. Recent advancements in programming models and software portability on the one hand, and reconfigurable hardware and domain-specific hardware design on the other [5, 7, 11], suggest that a modular approach that identifies common building blocks across domain boundaries might hold the key to both aforementioned challenges. Computational storage research exists on accelerating specific workloads or applications [5, 11, 14] as well as on emulating computational storage devices but a systematic study focusing on scientific computing workloads and modelling of suitable architectures and data distribution strategies is missing. Research Objectives: This project aims to advance the research on computational storage for scientific computing and artificial intelligence applications. It will investigate mechanisms to formalize, capture, model and evaluate computational storage in distributed environments. The project is structured into three primary objectives: Objective A: realize a survey and define ontologies and taxonomies for distributed computational storage systems from multiple angles across multiple domains: the application perspective (programming paradigm, data flows), the middleware perspective (workflow orchestration, resource management and data placement), and the system perspective (e.g., hardware, storage, compute) Objective B: establish the technical foundations to empirically assess hardware performance and decompose existing workflows and task characteristics on the one hand, as well as implement suitable computational storage testbeds (e.g. simulated [2,3,4], emulated [7, 10], and on realistic software stacks and physical hardware [6, 8, 9, 11]) Objective C: develop a methodology to faithfully model performance extrapolations to large-scale deployment scenarios as a means to identify and validate common computational storage building blocks Main activities: Envisioned Approach: To explore how computational storage can aid workloads in scientific computing and artificial intelligence, we will build upon previous work and active research of the members of the supervisory team in Germany and France. For objective A, the research methodology centers on analysing real-world scientific computing use cases in close exchange with domain scientists to identify computational storage opportunities. This work will establish ontologies and taxonomies for distributed computational storage systems from multiple angles across multiple domains. This work will be complementary to collaborations with the German Climate Computing Center (DKRZ) and the Parallel Computing and I/O group at Otto von Guericke University Magdeburg. Several surveys have taken snapshots of the state of the art of computational storage [12, 14] and its precursors or related concepts (e.g., active storage, near-data processing, processing in memory). The outcome of this aim will be a state of the art survey focusing on the applicability for scientific computing in the HPC, to cloud and edge computing continuum. Objective B is to establish suitable testbeds and modeling environments to study computational storage at scale. The work will build upon existing research of the KerData team to leverage system simulation to model large scale distributed systems [13] in addition to empirical platforms leveraging hardware emulation [7, 10], as well as realistic software stacks and physical hardware within Grid5000, Slices-FR or Chameleon Cloud and domain-specific experimental platforms together with collaborators from the different domain sciences. The targeted outcome of Objective B are proof-of-concept environments to run real-world computational workflows leveraging real operating system, middleware and device APIs. Objective C is to develop a methodology to faithfully model performance extrapolation to large-scale deployment scenarios will evaluate and validate the impact achievable through common computational storage building blocks. The methodology aims to study, for example, task and data placement strategies based on the needs of real-world computational science use cases but extrapolated to system scales not deployed in state of the art data centers today. The outcome will be a methodological framework to support middleware and domain-specific computational storage device development.

技能要求： 所需能力： 在计算机科学课程中出色的学术表现 分布式系统与数据管理系统的相关知识 强大的编程技能（Python、C/C++） 熟悉容器化环境（如 Docker、Podman、Apptainer、Kubernetes）的使用 具备开展高质量研究的能力与动力，包括将研究成果发表于相关学术平台 出色的口头及书面英语沟通能力 开放的心态、较强的融入团队能力及团队精神 以下条件更佳： 关于机器学习与数据分析方法的知识 在高性能计算（HPC）与大数据管理领域的专业经验

福利包： 补贴餐食 部分公共交通费用报销 休假规定：每年7周年假 + 因RTT（法定工作时间减少）额外享有10天假期 + 可能享受特殊假期（如照顾生病的子女、搬家等）。 远程工作可能性（在任职6个月后）及灵活的工作时间安排 提供专业设备（视频会议、计算机设备借用等） 社会、文化和体育活动及项目 职业培训机会 薪酬： 税前月薪为2300欧元 申请该职位

申请说明： 请在线提交简历、求职信及推荐信（如有）。 国防安全：本岗位可能位于受限区域（ZRR），此类区域依据保护国家科学技术潜力（PPST）的第2011-1425号法令界定。根据2012年7月3日关于PPST的法令，在主管部门作出批准决定后，由单位负责人授予区域准入许可。对于位于ZRR内的岗位，若主管部门作出不予批准的决定，将取消聘用。 招聘政策：作为多样性政策的一部分，法国国家信息与自动化研究所（Inria）的所有岗位均向残疾人士开放。