Python állások, munkák egyetemi végzettséggel

Where you belong: Where you belong: ...

• Developing a transformer-based neural network for the processing of NMR spectra
• Creating datasets from existing experiments within the CRC and from your own experiments, which are to be carried out during a research stay at KIT's Institute of Microstructure Technology (IMT)

• Which structural and semantic gaps exist in the Celonis OCPM representation of the current Mercedes-Benz planning process?
• Which semantic concepts of our approach can be formalized as a Semantic Layer (e.g., as an ontology / knowledge graph)?

You design and develop next-generation architecture at the intersection of multi-modal perception, foundation models, behavior learning, and end-to-end autonomous driving....

• Investigate and resolve data inconsistencies across complex end-to-end data pipelines
• Automate complex analytics across large datasets to validate outputs and identify anomalies or inconsistencies
• Debug pipeline failures and improve overall pipeline robustness

• Own and manage the fab CIM ecosystem, including MES transformation and factory automation systems
• Oversee equipment integration and communications across manufacturing systems
• Define and execute the strategic roadmap for manufacturing systems within a high-volume wafer fab environment

• Ensure state of the art testing across embedded, backend, and frontend domains to ensure high quality software products
• Create the test approach based on business and technical objectives
• Develop the test automation framework in close collaboration with other experts

• Develop both classical algorithms and modern machine learning techniques
• Implement algorithms efficiently on embedded hardware
• Optimize parameters for complex, hybrid image pipelines
• Develop innovative solutions for hardware-constrained challenges

• Translate R&D business and user requirements into effective system, process, and automation solutions
• Design, implement, test, and continuously improve automated workflows, AI-augmented data flows and AI Agents
• Ensure stable data flows, high data quality, and reliable system operations

• Deploy, configure, and manage network switches, fabric management software, and host-software stacks.
• Monitor fabric health, diagnose link errors, and resolve performance bottlenecks.
• Run benchmarks and characterize the interconnect performance.

• Software development and programming for control systems in the power generation sector
• Commissioning and testing of software in applications and systems
• Maintenance and upkeep of application software
• Assisting the sales support team

Architect the ML Platform: Design, implement, and maintain a robust MLOps infrastructure that enables researchers to move seamlessly from local experimentation to global production....

Drive SIEM maturity alongside our Tech Lead by continuously developing our Google Chronicle & SOAR platform, building and tuning correlation rules, and designing automated playbooks to optimize detection engineering....

• Provide technical support to scientific and industrial customers via phone, webchat, and email, focusing on post-sales and service inquiries
• Collaborate with local Engineering and global Technical Support teams to deliver accurate solutions and guidance

• Contribute to pioneering technical advances to the field of foundation models on structured data, as well as exploring world models for solving business problems.
• Define the data, training, and experimentation environment to enable scalable access to data and compute.

Design, build, and maintain scalable data platforms and data & AI solutions tailored to business needs, including the development of governance structures and access control concepts...

• Define and evolve data models for R&D entities
• Design and implement robust data pipelines and automated data quality checks
• Improve traceability and metadata management so data can be understood, trusted, and reused across teams

• Make optimization runs fast enough that customers iterate in minutes, not hours — across problems an order of magnitude larger than today
• Build the data backbone: ingest SAP/ERP exports, Excel reality, and operator knowledge into a clean, versioned, traceable model layer

• Designing and implementing end-to-end data flows from device data ingestion through transformation, storage and consumption
• Defining data models, structures and lifecycle concepts to ensure data quality, consistency and lineage

• Development and process engineering design of electrochemical CO₂ conversion processes
• Transfer of laboratory results into robust scale-up concepts for pilot scale
• Preparation and further development of mass and energy balances
• Development, application, and interpretation of process simulations