Bosch Group
Master Thesis Autonomous Agentic Control Design - Translating System-Theoretic Properties into Self-Improving Synthesis Loops(m/w/x)
Full-timeInternshipWith Homeoffice
Renningen
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BOBosch Group
Master Thesis in Continual Learning with Agentic Memories(m/w/x)
Renningen
Full-timeInternshipWith Home Office
AI/ML
Nejo AI Summary
Implementing LLMs in agentic settings for continual learning research. PyTorch and Git proficiency required. Hybrid work, Bosch-related use cases.
Requirements
- Master's degree in Computer Science, Mathematics, Machine Learning, or comparable with ML focus and very good grades
- Strong academic background in machine learning and applied mathematics
- Solid programming skills in deep learning with PyTorch and proficiency in Git
- Familiarity with job scheduling systems
- Practical knowledge of agentic systems and their implementation in research
- Background working with LLMs using PyTorch and Python
- Motivated, research-oriented, proactive, independent problem-solver, effective under deadline pressure
- Keen interest in independent problem solving
- Fluent English and beginner German
- Enrollment at university
- CV, transcript of records, examination regulations, list of previous code projects with descriptions
- Valid work and residence permit (if indicated)
Tasks
- Conduct comprehensive literature review on memory in agents
- Analyze existing benchmark implementations, datasets, and methods
- Build deep understanding of continual learning
- Adapt or implement benchmarks for Bosch-related use cases
- Write code to apply LLMs in agentic settings
- Focus on agent memory optimization
- Derive and implement methods to improve agent memory
- Evaluate performance on academic benchmarks and Bosch use cases
- Analyze scalability, robustness, and deployment potential
- Work within tight project timeline
- Submit results to major machine learning conferences
- Perform effectively under deadline pressure
Education
- Currently in higher education
Languages
- English – Native
- German – Basic
Tools & Technologies
- PyTorch
- Git
- Python
Benefits
Flexible Working
- Hybrid work model
Informal Culture
- Diversity and inclusion commitment
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Bosch Group and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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BOBosch Group
Master Thesis in Continual Learning with Agentic Memories(m/w/x)
Renningen
Full-timeInternshipWith Home Office
AI/ML
Nejo AI Summary
Implementing LLMs in agentic settings for continual learning research. PyTorch and Git proficiency required. Hybrid work, Bosch-related use cases.
Requirements
- Master's degree in Computer Science, Mathematics, Machine Learning, or comparable with ML focus and very good grades
- Strong academic background in machine learning and applied mathematics
- Solid programming skills in deep learning with PyTorch and proficiency in Git
- Familiarity with job scheduling systems
- Practical knowledge of agentic systems and their implementation in research
- Background working with LLMs using PyTorch and Python
- Motivated, research-oriented, proactive, independent problem-solver, effective under deadline pressure
- Keen interest in independent problem solving
- Fluent English and beginner German
- Enrollment at university
- CV, transcript of records, examination regulations, list of previous code projects with descriptions
- Valid work and residence permit (if indicated)
Tasks
- Conduct comprehensive literature review on memory in agents
- Analyze existing benchmark implementations, datasets, and methods
- Build deep understanding of continual learning
- Adapt or implement benchmarks for Bosch-related use cases
- Write code to apply LLMs in agentic settings
- Focus on agent memory optimization
- Derive and implement methods to improve agent memory
- Evaluate performance on academic benchmarks and Bosch use cases
- Analyze scalability, robustness, and deployment potential
- Work within tight project timeline
- Submit results to major machine learning conferences
- Perform effectively under deadline pressure
Education
- Currently in higher education
Languages
- English – Native
- German – Basic
Tools & Technologies
- PyTorch
- Git
- Python
Benefits
Flexible Working
- Hybrid work model
Informal Culture
- Diversity and inclusion commitment
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Bosch Group and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
Bosch Group
Industry
IT
Description
Das Unternehmen entwickelt hochwertige Technologien und Dienstleistungen, die das Leben der Menschen verbessern.
Not a perfect match?
- Bosch Group
Master Thesis Autonomous Agentic Control Design - Translating System-Theoretic Properties into Self-Improving Synthesis Loops(m/w/x)
Full-timeInternshipWith HomeofficeRenningen - Bosch Group
Senior Data Scientist for AI Enabling(m/w/x)
Full-timeWith HomeofficeSeniorRenningen - Bosch Group
Research Engineer for AI Validation(m/w/x)
Full-time/Part-timeWith HomeofficeSeniorRenningen - TRUMPF Werkzeugmaschinen SE + Co. KG
Abschlussarbeit Weiterentwicklung eines bestehenden Multi-Agenten Systems zur Steuerung von Werkzeugmaschinen(m/w/x)
Full-timeWorking StudentWith HomeofficeDitzingen - FEV.io GmbH
Master Thesis – AI in MBSE(m/w/x)
Full-timeRemoteExperiencedAachen, München, Stuttgart