PhD: Efficient optimisation for fast simulation under budget constraints
Where: Leiden University, 
Netherlands
What:
Within the NWO AI4optics project, this PhD develops AI-driven optimisation methods for accelerating optical simulation, a key bottleneck in the design of imaging and illumination systems. AI4optics brings together experts in computer science, mathematics and physics to develop AI-based methods for solving inverse problems in high-tech optical systems. By making optical simulation faster and more efficient, the project contributes to improved optical design workflows across a wide range of high-tech applications.
A major challenge in ray-tracing simulation is selecting the most informative rays from very large candidate sets without sacrificing simulation accuracy. In this project, you will develop optimisation methods that efficiently explore these large search spaces by exploiting meaningful similarity measures between rays. Building on Leiden University's previous work on distance-based subset optimisation, originally developed for optimal optical filter selection, you will investigate structured sampling strategies together with quality-diversity and generative optimisation methods to accelerate simulation-driven optical design.
The project also explores learning-based optimisation to further reduce computational cost. You will investigate warm-starting optimisation from previously discovered solutions, transferring knowledge between related optimisation problems and removing redundant search across optimisation runs. In parallel, you will develop AI methods for efficiently predicting phase-space partitions and design novel heuristic algorithms for mixed-variable, multi-objective optimisation of imaging optics involving both continuous geometric parameters and discrete design choices such as lens materials.
Who: Anna Kononova, a.kononova@liacs.leidenuniv.nl
When: Until 2026-09-01 05:59
Presented at next GECCO?: yes
PhD: Robust Optimisation of Complex Optical Systems for Imaging
Where: Leiden University, Netherlands
What:
Within the NWO AI4optics project, this PhD develops robust and explainable optimisation methods for advanced imaging optics. AI4optics brings together experts in computer science, mathematics and physics to develop AI-based methods for solving inverse problems in high-tech optical systems. The project aims to make optical simulation, design and reconstruction easier using AI, ultimately contributing to smaller, more powerful and energy-efficient integrated circuits, reduced light pollution and improved Earth observation from space.
Traditional optical design workflows often produce systems that perform well under ideal conditions but degrade significantly due to manufacturing imperfections, alignment errors or environmental variations. In this project, you will develop robust multi-objective optimisation algorithms that explicitly account for these uncertainties during the design process. By combining evolutionary and hybrid optimisation with uncertainty quantification, you will identify not just optimal but stable optical designs. The research includes mixed-variable optimisation problems involving both continuous and discrete design parameters, as well as competing objectives such as image quality versus cost and compactness versus stray light.
A second research direction focuses on explainable AI for optical design. You will develop methods that make optimisation more transparent by revealing which design parameters most strongly influence performance and robustness. Building on recent advances in explainable AI, surrogate modelling and sensitivity analysis, you will investigate how to explain optimisation outcomes, identify the causes of design failures and provide actionable insights to optical designers and engineers.
Who: Anna Kononova, a.kononova@liacs.leidenuniv.nl
When: Until 2026-09-01 05:59
Presented at next GECCO?: yes
PhD: PhD candidates - various topics in machine learning and optimization
Where: Ghent, Belgium, 
Belgium
What:
The SUMO research cluster at the Internet & data science lab is always looking for motivated PhD students. Contact us for more information and available topics.
https://sumo.ilabt.imec.be/home
https://sumo.ilabt.imec.be/home/research
Who: ivo.couckuyt@ugent.be
When: Until 2026-11-30 06:00
Presented at next GECCO?: yes
other positions in industry: Software Engineer (Research and Development)
Where: Tokyo, 
Japan
What:
We are seeking a versatile and experienced Software Engineer to bridge the gap between our advanced ML research and our user-facing products. Unlike a traditional backend role, this position requires a generalist mindset: you will build the tools that accelerate our research (data pipelines, benchmarking systems), develop the full-stack infrastructure for our products (user portals, internal tools), and assist in managing our compute resources. You will be the “glue” that turns research capabilities into deployed, usable software.
Key Responsibilities
Accelerate ML Research (Data & Pipelines) – Design and build robust data collection pipelines and establish automated benchmarking systems. You will work closely with researchers to ensure they have the data and evaluation metrics needed to iterate on models quickly.
Full-Stack Product Development – Develop user-facing web portals and internal dashboards. This includes building product-related infrastructure, handling user authentication, and creating intuitive interfaces that allow customers (and internal teams) to interact with our AI models.
Required Qualifications
3+ years of experience in software engineering, with a willingness to work across the full stack.
Strong proficiency in Python (essential for our ML ecosystem) and experience with a second language like Go, Node.js, or TypeScript.
Experience with web development frameworks (e.g., React, Next.js, Vue, or similar) to build portals and dashboards.
Experience working with cloud platforms (GCP, AWS, or Azure) and containerization (Docker).
Proven experience building and deploying mission-critical, scalable applications on a major cloud platform (GCP, AWS, or Azure).
Preferred Qualifications
Language ability: It is preferable if the candidate can communicate in English at a level required to collaborate on technical projects with international team members. Japanese language ability is a huge plus, since the role is based in Japan, but not strictly required. If you are a native Japanese speaker, or passed JLPT N1 or JLPT N2, please do mention this in your application.
As the role is based in Tokyo, Japan, it is preferable if the candidate already lives in Japan, or has lived here before.
Experience using generative AI technologies like LLMs or AI agents is a plus.
Experience using AI-assisted coding tools such as Claude Code, GitHub Copilot, Devin, Cursor, etc in software development is a plus.
Experience designing and managing production-ready APIs and databases.
Experience implementing DevOps best practices, including Infrastructure as Code (IaC) and setting up CI/CD pipelines.
Experience serving ML models and monitoring their performance.
Who: https://sakana.ai/careers/software-engineer-research-and-development/
When: Until 2026-08-30 15:00
Presented at next GECCO?: yes
research engineer in industry: Member of Technical Staff
Where: Tokyo, Japan
What:
We are occasionally hiring researchers who are passionate about novel approaches to develop the next generation of nature-inspired foundation AI models together with us. We are looking for candidates who we believe have the potential to build great things — people who have passions in pushing the boundaries of areas such as evolutionary computation, generative AI, open-endedness, collective intelligence, artificial life and autonomous agents.
Ideal candidates can demonstrate this by highlighting previous projects or publications related to AI that they are proud of showcasing. For candidates with academic backgrounds, we will be interested in your publications in venues such as NeurIPS, ICLR, GECCO, ALIFE, MLSys, JMLR, ICML, IROS, or other peer-reviewed journals or conferences. The ability to write high quality code and having experience contributing to popular open-source software projects will be a strong asset.
Candidates will be expected to be physically based in Tokyo, but we will provide assistance with the Visa application process if needed. We will be offering positions in the form of 4-month internships or part-time student researcher positions (for graduate students already based in Japan).
For candidates interested in longer-term, potentially full-time, applied research and engineering roles, having Japanese language fluency is required, and experience working with Japanese institutions will be beneficial. Please apply for the Applied Research Engineer or Software Engineer roles.
For candidates with a strong engineering background interested in a 1-year or full-time position in our research team, please consider the Software Engineer (Research and Development) role instead. (We are actively recruiting for this role instead).
Who: https://sakana.ai/careers/member-of-technical-staff/
When: Until 2026-08-30 15:00
Presented at next GECCO?: yes
PhD: PhD candidate, Generative (X)AI for the Discovery of Domain-Specific Optimizers
Where: Leiden, Netherlands
What:
Within the NWO AI4optics project, Leiden University leads Work Package B.2, “Generative (X)AI for the discovery of domain-specific optimizers”. This project advances new methods for automatically discovering optimization algorithms tailored to specific optical design problems, in both imaging and illumination (non-imaging) optics.
The approach builds on Leiden's own evolutionary framework for automatic black-box optimization algorithm discovery (LLaMEA), which places a Large Language Model inside a generate-test-refine loop: the LLM proposes solver code, the candidate is benchmarked, and the LLM is asked to improve it based on structured feedback — repeated many times to evolve families of algorithms that rival or beat hand-crafted baselines. In this project, you will further integrate optical domain knowledge, so that discovered solvers can tackle real-world optical physics problems, and combine LLM-driven code generation with in-the-loop hyper-parameter optimization (e.g. Bayesian optimization), so the LLM can focus on algorithmic structure while numerical tuning is handled automatically.
A second, equally important line of research is explainability. Building on Leiden's “code evolution graphs” and mechanistic interpretability research, which analyze how LLM-generated algorithms evolve through iterative mutation and refinement, you will develop richer visualization and explanatory methods that reveal which code changes and evolutionary strategies actually improve solver performance, linking these directly to improvements in key optical performance metrics such as wavefront error and stray light. This will help domain experts validate and trust automatically discovered solvers. You will also design prompt templates and retrieval-augmentation techniques that embed physics- and optics-specific constraints into the code generation process, and develop curated benchmarks reflecting industrially relevant optics use cases.
Who: Niki van Stein - n.van.stein@liacs.leidenuniv.nl
When: Until 2026-08-30 22:00
Presented at next GECCO?: yes
PhD: PhD in LLM-Augmented Automated Algorithm Design
Where: St Andrews, 
United Kingdom
What: You will investigate how to use LLMs to build better algorithms for solving challenging AI problems, by automating the tedious parts of the algorithmic design process. You'll apply evolutionary algorithms and other state-of-the-art black-box optimisation methods to improve code and create new code from scratch. You'll have a look at hard problems of practical relevance, e.g. combinatorial scheduling.
Who: Lars Kotthoff, lk223@st-andrews.ac.uk
When: Until 2026-12-15 00:00
Presented at next GECCO?: no
other positions in industry: Research Engineer
Where: London / Bristol or home based, United Kingdom
What: We’re seeking a research engineer to help build our scientific computing products, support early-access customers with their workloads, and work with our wider customer base to construct data platforms, digital twins, and optimization systems.
Who: Andrew Morgan, andrew@gamakon.ai
When: Until 2026-08-31 06:00
Presented at next GECCO?: yes
teaching position: Recruitment of High-Level AI Academic Talents
Where: Hefei, Anhui Province, 
China
What:
Category III Talent:
Qualifications: Outstanding young talents with broad academic vision and innovative thinking, who have achieved remarkable academic results and hold significant influence in their fields.
Responsibilities: Oversee talent development and the building of academic teams for the discipline; lead the application for and establishment of provincial-level key laboratories or research platforms.
Category IV Talent:
Qualifications: Candidates with excellent academic achievements and outstanding potential for future scientific research.
Responsibilities: Lead or play a key role in discipline and team development; continuously publish high-quality journal papers, win prestigious competition awards, or achieve industry-academia-research integrated technology transfer.
Category V Talent:
Qualifications: Candidates holding a doctoral degree, generally under 35 years of age.
Responsibilities: Actively participate in the college's discipline and platform development, and actively engage in international academic exchanges.
Who: Changhe Li; lichanghe@aust.edu.cn
When: Until 2026-12-30 16:00
Presented at next GECCO?: yes
research engineer in academia: Research Software Engineer
Where: Paris, 
France
What: We are looking for a research software engineer to join our team at LIP6 in Paris. You will help us on a variety of research projects, centered around our tools for benchmarking optimization heuristics (IOHprofiler). The project is funded by the ERC grant 'dynaBBO' of Carola Doerr. If you want to know more, please feel free to reach out to us.
Who: diederick.vermetten@lip6.fr
When: Until 2026-09-29 22:00
Presented at next GECCO?: yes
research engineer in industry: Research Scientist
Where: San Francisco, 
United States
What: We are hiring new PhDs to work on evolutionary optimization of LLMs and multiagent systems.
Who: risto@cognizant.com
When: Until 2026-12-31 08:00
Presented at next GECCO?: yes