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Yinan Yu

2 months ago

Postdoc in LLM Agents for Research Automation Chalmers University of Technology in Sweden

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

Deadline

Expired

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Country

Sweden

University

Chalmers University of Technology

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Keywords

Computer Science
Machine Learning
Information Technology
Deep Learning
Artificial Intelligence
Software Engineering
Debugging
Multi-agent System

About this position

Chalmers University of Technology invites applications for a postdoctoral position in LLM Agents for Research Automation, based at the Division of Computing Science, Department of Computer Science and Engineering. This research opportunity is embedded within AIXLab (www.usableai.se), a collaborative environment dedicated to advancing usable, reliable, and impactful AI systems. The project focuses on developing next-generation LLM agents to support deep learning research, model development, and code generation, aiming to accelerate research and innovation in both academic and industrial settings.

The Department of Computer Science and Engineering, a joint department of Chalmers and the University of Gothenburg, is internationally recognized for its research excellence and strong industry links. The Division of Computing Science advances secure and trustworthy software and systems, spanning programming languages, tools, and practical methods for dependable digital infrastructures. AIXLab develops methods and tools for deep learning, foundation models, LLM agents, automated machine learning, and AI-enabled software engineering, with a strong emphasis on practical evaluation and industry collaboration. Application areas include software development, automotive, healthcare, accessibility, sustainability, and industrial AI.

The research project investigates LLM agents for autoresearch, focusing on supporting deep learning research and software development workflows. The goal is to create agentic AI systems that assist researchers and developers in hypothesis formulation, experiment design, model implementation, code generation and revision, debugging, result analysis, research knowledge management, and reproducibility improvement. The project covers the full lifecycle of technical research and development, from idea generation and literature exploration to model implementation, prototyping, testing, evaluation, and documentation. Developed methods and prototypes will be evaluated with industrial partners to ensure practical relevance and robustness.

Requirements: Applicants must hold a doctoral degree in computer science, artificial intelligence, machine learning, or a closely related field by the time of employment decision. Strong research experience in deep learning, machine learning, AI-assisted software development, or automated machine learning is required. Programming skills in Python and experience with deep learning frameworks (PyTorch, TensorFlow, JAX) are mandatory. Excellent English communication skills, ability to work independently, and collaborative skills are expected. Experience with large language models, foundation models, LLM agents, retrieval-augmented generation, multi-agent systems, code generation, program synthesis, automated debugging, software testing, program repair, deep learning model development, automated machine learning, neural architecture search, meta-learning, data-centric AI, automated experimentation, and relevant publications will strengthen the application. Teaching experience is desirable.

What you will do: Develop LLM-agent methods and prototypes for deep learning workflows, software development, code generation, debugging, testing, and documentation. Evaluate methods in controlled experimental environments and with industrial partners. Publish scientific papers in AI, machine learning, and software engineering venues. Engage in teaching and thesis supervision at undergraduate/master’s level. The position is meritorious for future roles in academia, industry, or the public sector.

Contract terms: Temporary full-time employment for two years, with possibility of a one-year extension. Physical presence at Chalmers is required throughout employment. A valid residence permit must be presented by the start date. As a postdoc at Chalmers, you are an employee and enjoy all employee benefits, including healthcare, parental leave, subsidized day care, and free schools. Chalmers is committed to gender balance, equality, and inclusion, and offers Swedish courses for non-native speakers.

Application procedure: Submit your application via the online form. Attach your CV (including publications and teaching experience) and a personal letter (introduction, research summary, future goals). Applications must be written in English and attached as PDF files. Incomplete applications and applications sent by email will not be considered. Contact details to references will be requested after the interview. Deadline: July 3rd, 2026. For questions, contact Assistant Professor Yinan Yu at [email protected].

Chalmers University of Technology is a leading institution in technology and natural sciences, fostering innovation and global commitment. The university promotes knowledge and technical solutions for a sustainable world, with a strong focus on scientific excellence and collaboration with society.

Funding details

Available

What's required

Applicants must hold a doctoral degree in computer science, artificial intelligence, machine learning, or a closely related field by the time of employment decision. Strong research experience in deep learning, machine learning, AI-assisted software development, or automated machine learning is required. Strong programming skills, preferably in Python and modern software development environments, and experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX are mandatory. Excellent written and verbal communication skills in English are required. Ability to work independently, drive research tasks, collaborate in a research group, and contribute to joint projects and scientific discussions is expected. Experience with large language models, foundation models, LLM agents, retrieval-augmented generation, multi-agent systems, code generation, program synthesis, automated debugging, software testing, program repair, deep learning model development, automated machine learning, neural architecture search, meta-learning, data-centric AI, automated experimentation, and publications in relevant venues will strengthen the application. Teaching experience is desirable.

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