post-doc: Postdoctoral Research Fellow
Where: Exeter, 
United Kingdom
What: The position is part of the project: “Under-Land: Understanding and Visualising Complex Optimisation Landscapes”. This project aims to develop visualisation and analysis methods to aid in the comprehension of optimisation landscapes. The goal is to convey real-world optimisation scenarios, including multiple competing objectives of interest, complex problem representations, and uncertainty, in a compact form, such as a network (graph). The collaborative project is led by Prof. Gabriela Ochoa at the University of Stirling, with a team at the University of Exeter. This Leverhulme Trust funded post is available from 1 October 2025 to 30 September 2028. Further details are available at: https://www.jobs.ac.uk/job/DOJ061/postdoctoral-research-fellow
Who: Jonathan Fieldsend
When: Until 2025-09-08 22:59
Presented at next GECCO?: yes
post-doc: AutoLearn-SI Opens Applications for New Postdoctoral Positions
Where: Jožef Stefan Institute, Ljubljana, 
Slovenia
What:
Organisation / Company: Jožef Stefan Institute, Ljubljana, Slovenia
Department: Department of Computer Systems (one position)/ Department of Knowledge Technologies (one position)
Research Field: Artificial Intelligence
Researcher profile: Postdoctoral researcher
Country: Slovenia
Application deadline: 31 December 2025 – 23:59 (Europe/CET)
Type of contract: Temporary (1.5 years)
Job status: Full-time
Hours Per Week: 40
Offer starting date: 1st June 2026
Offer description: We offer two postdoctoral positions in Automated Machine Learning and Optimization (AutoML and AutoOPT), starting on June 1st, 2026, as part of the Horizon Europe (HE)-funded project AutoLearn-SI (101187010). The primary aim of AutoLearn-SI is to establish a permanent ERA Chair research group specializing in AutoAI, focusing on AutoML and AutoOPT. This initiative will serve as a key hub for research excellence in Slovenia and its surrounding region, ensuring sustained quality, increased visibility, and stronger integration within the European Research Area (ERA). The ERA Chair will emphasize leveraging benchmarking data for automated ML and optimization, driving targeted research in three Pillar themes – Experimental Databases, Representation Learning, and Automated Algorithm Selection/Configuration – and two Horizontal themes: Single-objective Optimization and Multi-label Classification.
Roles and Responsibilities:
The main part of your work will be carried out at the “Jožef Stefan” Institute (Ljubljana, Slovenia, https://ijs.si/ijsw) within the newly created AutoLearn-SI ERA Chair supported by the Computer Systems Department (https://cs.ijs.si/) and the Department of Knowledge Technologies (https://kt.ijs.si/).
The postdoctoral project is funded for one and a half years and would start by 1st June 2026 or later.
You will also be required to participate in the training activities and workshops organized by the ERA Chair AutoLearn-SI. As a staff member within the ERA Chair AutoLearn-SI, you are also expected to contribute to the dissemination of your project´s results through public engagement and various scientific platforms.
Research Topics: The postdoctoral projects will explore concepts in experimental databases, representation learning, and automated algorithm selection and configuration, with a primary focus on single-objective optimization and multi-label classification. There is also potential to extend these concepts to other areas, such as large language models, multi-objective optimization, and natural language processing.
The ideal candidate
The primary goal of the postdoctoral position is to equip candidates for data-driven careers. Eligible applicants must possess a PhD degree in Computer Science, Electrical Engineering, Mathematics, Physics, or a related discipline, with a solid foundation in Data Science. Key qualifications include:
Required selection criteria:
A PhD degree in a relevant field.
Experience in Data Science.
Publication record in computer science or a related field.
Proficiency in programming languages such as Python or R.
Preferred selection criteria:
Experience in research project work.
Proficiency in written and spoken English.
We offer
AutoLearn-SI, based at JSI—Slovenia's largest public research institute—spans natural sciences, life sciences, and engineering. With a team of over 930 staff, JSI emphasizes high-level scientific output and the development of young scientists. Its governance includes a Board of Governors, a Director, a Scientific Council, research departments, centers, and support units. The Scientific Council, composed of senior researchers with four-year terms, oversees research and education. JSI's expertise covers AI, computer systems, robotics, biotechnology, nanotechnology, and environmental sciences.
Led by the ERA Chair holder, AutoLearn-SI integrates two key JSI departments: the Computer Systems Department and the Department of Knowledge Technologies, driving success in research, development, and training. Positions at the ERA Chair are open to outstanding national and international researchers, with the possibility of contract extensions beyond the project term. Recruitment will follow an open, transparent process under Slovenian law and European Commission oversight, adhering fully to the Code of Conduct for the Recruitment of Researchers1, with attention to gender and diversity.
The salary will be determined in accordance with Slovenian regulations for research positions, considering four years of employment at the institute. We offer a gross annual salary starting around € 2830 (monthly) plus regular legislation-based improvements for four years. In addition, a meal allowance will be provided for each working day, and commuting expenses will be reimbursed in accordance with Slovenian law.
We provide engaging and stimulating opportunities within a dynamic international academic environment, along with a supportive and inclusive workplace featuring dedicated and collaborative colleagues.
Jožef Stefan Institute honors and respects the diversity of our staff and provides equal opportunities to all employees and qualified applicants regardless of sex and/or gender, nationality, race or ethnicity, health condition, disability, religion or faith, age, social status, sexual orientation, gender identity or gender expression, family status or any other personal circumstance.
Application procedure:
To apply for this position, kindly provide: i) a letter of motivation including a 1-page statement of your research interests, relevant skills and experience; (ii) a CV including publication list (describe your role in each publication); (iii) copies of relevant certificates (e.g., diplomas and trasncripts for PhD studies - original oficial documents with English translation) and (iv) names and contact details of at least two referees willing to write confidential letters of recommendation. All materials should be attached as a single PDF file.
For interested applicants, please submit your application by sending an email to: info-autolearnsi at ijs.si.
Deadline:
The closing date for all applications is December 31st, 2025
Enquiries:
For further general information about the Postdoctoral Candidate positions, you may visit the webpage at https://autolearnsi.eu/ or send an email to info-autolearnsi at ijs.si.
For further specialist information regarding this position, you may contact the Assistant. Prof. Dr. Tome Eftimov or Assistant Prof. Eva Tuba at JSI.
Selection process
1st stage: Review of application documents to verify the applicant's eligibility and if the work and/or educational experiences would fit the position requirements
2nd stage: Panel interview within the ERA Chair
3rd stage: Internal deliberation and ranking of candidates. This will be based on the conducted interview, work/educational experiences, and letter of recommendation from your referees.
Who: info-autolearnsi@ijs.si
When: Until 2025-12-30 23:00
Presented at next GECCO?: yes
post-doc: Cartesian Genetic Programming for 3D biomedical image analysis
Where: Toulouse, 
France
What:
In the past few years, we have used and developed a Cartesian Genetic Programming (CGP) based approach to segment biomedical images. This approach has been showed to be efficient to learn from very few images while keeping high level of interpretability of generated pipelines. Recently, we have proposed the Multimodal Adaptive Graph Evolution (MAGE) approach, which extends the CGP approach to a multi-chromosome representation in order to treat multimodal data. With MAGE, we were able to classify biomedical images and, together with biologists and medical experts, explain the mechanism of decision taking used by the best models. The benefit of our approach are twofold in comparison to SOTA deep learning approaches: (1) CGP/MAGE requires a limited amount of data to learn while keeping good generalisability capacities; (2) CGP/MAGE generates interpretable data analysis pipelines. However, the generated pipelines are still suffering of few points of under-performance in comparison to SOTA deep learning approaches.
The aim of this postdoc is to continue to develop this approach by extending the function libraries to be able to target 3D images, in particular in multimodal brain images. Additionally, we would like to explore automatic abstraction mechanisms in order to generate more complex graphs and, hopefully, improve the performance of the generated pipelines while keeping high level of interpretability.
Duration: 18 months
Location: Toulouse, University Toulouse Capitole, IRIT-CNRS
Salary: based on experience
Who: Sylvain Cussat-Blanc, sylvain.cussat-blanc@ut-capitole.fr
When: Until 2025-12-30 23:00
Presented at next GECCO?: yes
post-doc: Evolution of Agent-Based Behavior with Genetic Programming
Where: Toulouse, France
What:
Over the past few years, we have a develop a platform, named ISiCell (isicell.irit.fr), that allows to co-design agent-based models for cell biology. This platform allows to visually develop the agent/cell behaviors, manually setup the model’s parameters and visualize the results in live. Additionally, we have developed a Python wrapper that allows to access to any parameters and measured values within the simulation. We are now using the platform to produce models together with biologists in different domains such as oncolytic viruses, neurodevelopment and inflammatory processes.
While plenty of algorithms already exist to explore parameters (including evolutionary algorithms), the aim of this post-doc is now to allow the exploration of the model behavior space. We want to use genetic programming or an equivalent approach to allow for the modification of existing rules of a model and the proposition of new rules/hypothesis. The aim is to be able to evaluate both the efficiency of the newly generated models and the distance to the original human-made model. Indeed, we want to generate models that are close to the original one (thus limiting the number of automatically made hypotheses) and performing better in term of comparison to biological data.
The hired postdoc fellow will be hosted in a team composed of PhD students and postdoc both specialists in evolutionary computation, genetic programming (CGP in particular) and agent-based modeling. He/She will work together with biologists from either or both the Centre de Recherche en Cancérologie de Toulouse (CRCT - INSERM) and the RESTORE lab (INSERM) both to get specific data for the model calibrations and to evaluate the biological correctness of automatically generated models.
Duration: 12 to 18 months
Location: Toulouse, University Toulouse Capitole, IRIT-CNRS
Salary: based on experience
Who: Sylvain Cussat-Blanc, sylvain.cussat-blanc@ut-capitole.fr
When: Until 2025-12-30 23:00
Presented at next GECCO?: yes
research position: Assistant Professor
Where: Corvallis, Oregon, 
United States
What:
The School of Mechanical, Industrial, and Manufacturing Engineering (MIME) at Oregon State University (OSU) invites applications for a full-time, nine-month, tenure-track academic faculty position within the area of field industrial engineering at the rank of Assistant Professor.
Appointment at the Assistant Professor rank is anticipated; however, appointment at a promoted rank may be considered depending upon the qualifications of the successful candidate.
This position is expected to establish a research program exploring theory and application in cognitive systems engineering and specifically human-autonomy teaming. Areas of application interest include, but are not limited to:
Embodied AI/robotics
AI in healthcare systems
AI in transportation systems
This position is expected to establish, a thriving track record of federal and industry funding and partnerships to grow an enterprise around their areas of research.
The Oregon State College of Engineering has committed to being a national model of inclusivity and collaboration. We strive to develop a community of faculty, students, and staff that is inclusive, collaborative, diverse, and centered on student success. As such, we seek applicants who demonstrate a commitment to diversity and inclusion, including efforts promoting equitable outcomes among learners of diverse and underrepresented identity groups. Further, we seek to broaden our capacity to advance student success across individual identities, racial/ethnic categories, and socioeconomic backgrounds.
Who: https://jobs.oregonstate.edu/postings/169874
When: Until 2025-12-03 08:00
Presented at next GECCO?: yes
research engineer in industry: Research Scientist
Where: San Francisco, CA, United States
What:
- Cognizant AI Lab**
Cognizant works with an incredible diversity of organizations across the globe, using AI to improve decision-making, robustness, forecasting, and growth at every level of operation. Within Cognizant, Cognizant AI Lab (CAIL; https://cognizantailab.com) serves as the center of excellence for pioneering AI research. The team works to develop novel approaches to solve both fundamental scientific problems and challenges from real-world applications. The work done by CAIL serves to inspire and catalyze real-world applications implemented by Cognizant, and reciprocally, real-world challenges encountered in Cognizant’s diverse ecosystem of applications serve to inspire foundational research at CAIL.
- Your role:**
As a research scientist, you will work with the CAIL research team to develop novel approaches to fundamental scientific problems and challenges from real-world applications, using core technologies such as LLMs, evolutionary algorithms, and other machine learning and AI techniques. With an explicit focus on AI for Good applications alongside basic research, the team envisions a world where AI systems are safe, robust, sustainable, long-lived, and inspiring. The team's current research is focused around areas including, but not limited to:
- AI for decision-making
- AI orchestration
- Trustworthy AI
- Open-ended AI
- Sequential/time-series domains
- Multi-agent systems
- ALife
- LLMs
- Evolutionary computation
- Key Responsibilities:**
- Work with the CAIL research team to develop original ideas that can contribute to the AI community
- Design experiments and evaluation methodologies for testing these ideas
- Implement novel algorithms and evaluation frameworks
- Manage experiments, analyze results, and iterate rapidly
- Communicate ideas and results to a larger audience
- Publish papers based on this work
- Advise AI engineers on the development of practical applications
- Qualifications:**
- Should have a PhD in Computer Science or another technical field.
- Passion for AI research and AI for Good.
- 3+ years of experience in AI or ML research.
- Publications at venues such as ICLR, NeurIPS, GECCO, ALife, AAAI, IJCAI, ICML, etc.
- Strong implementation skills.
- Experience with LLMs.
- Strong problem-solving and analytic skills.
- Strong attention to detail and ability to work independently.
- Excellent verbal and written communication skills.
- Important Note:** If you are interested in this position, or have any questions about this job posting, feel free to contact the hiring manager Xin Qiu (xin.qiu@cognizant.com) or Elliot Meyerson (elliot.meyerson@cognizant.com). While at GECCO, you can also talk in person with the CAIL research lead Risto Miikkulainen (risto@cognizant.com).
Who: Risto Miikkulainen, risto@cognizant.com
When: Until 2025-08-31 07:00
Presented at next GECCO?: yes
post-doc: Time complexity Analysis of Bio-Inspired Computation
Where: Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, 
China
What:
The aim of the project is to develop methodologies for explaining and predicting the performance of bio-inspired search heuristics. The methodology will be used to derive and extend the theoretical foundations of bio-inspired computation. Both theoretical and empirical analyses can be performed.
Selected topics include the performance analysis of:
a) Population-based search heuristics: highlighting their advantages over single-trajectory algorithms and/or the advantages of recombination over mutation-only algorithms
b) Algorithm configurators: how to evolve the optimal parameter settings for the meta-heuristic
c) Hyper-heuristics: how to evolve the meta-heuristic itself
d) Genetic programming: how to evolve computer programs effectively;
Person Specification
• PhD in computer science (or close to completion) or closely related area
• Publication record commensurate with career stage in high impact journals and conference proceedings
• Expertise in some or all of the following:
o Theory of bio-inspired computation
o Algorithm time complexity analysis and computational complexity
o Computational complexity analysis of randomized algorithms
o Analysis of stochastic processes
o Experimental analysis of bio-inspired computation techniques
• Excellent computer programming skills (JAVA, C)
• Experience of Latex, SVN, GIT or analogue
Main Duties and Responsibilities
• Contribute to the development of mathematical techniques for the time complexity of bio- inspired optimization heuristics
• Perform runtime analyses of bio-inspired search heuristics for combinatorial optimisation problems
• Investigate the impact of algorithmic parameters on the overall performance and the impact of automatic adaptation of the parameters
• Carry out computational experiments required for the achievement of the research goals
• Plan work activities to ensure deliverables and deadlines are met while continuously
monitoring progress
• Disseminate the results via project meetings, conference papers, conference presentations
and journals of the highest quality as well as impact delivery activities (special session and
tutorial organization at conferences
• Collaborate closely with research collaborators world-wide
• Undertake activities to increase own leadership and professional standing in the community
and international scale
• Contribute to the intellectual growth of the research group by co-supervising research
students
About the University and department
Established in 2010 with the mission to reform Chinese tertiary education and become a top-notch international research university, SUSTech was launched in the tech capital city of Shenzhen. SUSTech is becoming the important epicentre for China’s science and technology academic research and for the cultivation of innovative minds. The rapid ascent of SUSTech onto the global stage is remarkable. In the Times Higher Education (THE) World university Rankings 2023, it ranked 8th in Mainland China and 166th among the universities in the world. In THE Young Universities Rankings 2024, SUSTech was ranked 1st in China.
The SUSTech campus sits in the rolling hills of Nanshan District, with the verdant green lawns reflecting the environmentally friendly policies of the university. The natural and tranquil environment combines perfectly with the modern style of Shenzhen and its convenient location. With the campus covering an area of nearly 2 square kilometers, there is plenty of room for students to cogitate and consider their research or relax and enjoy their lives on campus. With students transiting the campus on foot, by bike or utilizing our convenient electric shuttle buses, its commitment to environmental sustainability is strong.
Located in the dynamic metropolis of Shenzhen, China’s Silicon Valley, SUSTech is centered on a thriving ecosystem of entrepreneurship, innovation and research. Some 43 per cent of the total PCT patent applications in China came from Shenzhen in 2017, and the city shows no signs of slowing down. As China’s research and development center, it is the perfect place for entrepreneurs, researchers and innovators alike to make their home alongside tech giants such as Huawei, Tencent, BYD, DJI, BJI and Mindray.
Shenzhen is also only distant 17 minutes from Hong Kong city centre by high speed train and about
an hour from Macau by ferry. The successful candidate will join the recently established AI-Theory Lab in the department of Computer Science and Engineering with world-leading expertise in bio-inspired computation.
Salary
332,550-450,000 RMB per annum for 2 years.
Meal supplement and festival expenses allowances as well as high/low temperature subsidies are also provided. Funding is available for conference attendance and collaborative research visits to related research groups in organisations world-wide. The AI-Theory Lab at SUSTech maintains effective collaborations with all the research organisations with major expertise in the theory of bio-inspired computation world-wide.
Line Manager
Professor Pietro S. Oliveto is Chair of the AI-Theory Lab at SUSTech. His main research interest is the rigorous performance analysis of bio-inspired computation techniques. Further information can be accessed via his personal webpage: https://peteroliveto.github.io
Who: Pietro S. Oliveto, olivetop@sustech.edu.cn
When: Until 2025-10-29 16:00
Presented at next GECCO?: no
PhD: Theory guided algorithm design in bio-inspired optimisation
Where: Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China
What:
Bio-inspired meta-heuristics, such as evolutionary algorithms, genetic algorithms or artificial immune systems are general purpose algorithms that mimic powerful mechanisms from nature such as the natural evolution of species or the collective intelligence of animals with the goal of solving complex optimisation problems. They have been applied successfully to a broad range of problems in various disciplines with remarkable success. They are particularly useful in settings where limited knowledge about the problem is available (black-box optimisation) and evaluating candidate solutions is the only means of learning about the problem at hand.
In recent years theoretical analyses have emerged that provide mathematically proven statements regarding the performance of bio-inspired algorithms. They rigorously estimate the expected time required by the algorithms to find a satisfactory solution for various optimisation problems. Such analyses use mathematical techniques drawn and extended from the fields of randomised algorithms, probability theory and computational complexity. The results allow for insights into the working principles of bio-inspired meta-heuristics, enable the assessment of parameter choices and design aspects, and ultimately guide towards the design of more powerful algorithms.
This studentship is aimed at exploring the effectiveness of these theory-guided algorithms on combinatorial optimisation problems with practical applications, thus aiming to bridge the gap between theoretical and practical research. This is a valuable opportunity to do research at the intersection between theoretical computer science and artificial intelligence in practice, drawing from proven theoretical insights for the design of improved bio-inspired search heuristics and their empirical evaluation on real world applications.
The successful applicant will develop expertise in one or more promising research areas of his/her choice in this wide research area.
Possible topics include the performance analysis of:
a) Population-based meta-heuristics: highlighting their advantages over single-trajectory algorithms and/or the advantages of recombination over mutation-only algorithms;
b) Algorithm configurators: how to evolve the optimal parameter settings for the meta-heuristic;
c) Hyper-heuristics: how to evolve the meta-heuristic itself;
d) Genetic programming: how to evolve computer programs effectively.
Candidate Requirements
Applicants must have recently completed a first class (or equivalent) undergraduate degree or a Masters degree with distinction in Computer Science or close to completion. Outstanding applicants from Mathematics are also encouraged to apply. The successful applicant must have excellent analytical and computational skills. He/She must be an excellent team player who can work independently and communicate well with others. If English is not their first language, they must have an IELTS score of 6+ overall, with no less than 6.0 in each component or TOEFL of 75+. Since the project is theoretically challenging, strong mathematical and probability theory skills are required. This position is open to all qualified candidates independent of nationality.
Funding and Eligibility
This studentship covers the full tuition fee and provides a tax-free stipend from RMB 150,000 to RMB 200,000 (approx. GBP 15,000/EUR 17,000 to GBP 20,000/EUR 23,600) per annum for four years. The studentship also covers free on- campus accommodation and subsidised on-campus meals in over 10 different cafeterias/canteens as well as popular western and eastern fast food chains. Funding is available for conference attendance and collaborative research visits to related organisations world-wide. The AI-Theory Lab at SUSTech maintains effective collaborations with all the research organisations with major expertise in the theory of bio-inspired computation world-wide.
About the University/Department
Established in 2010 with the mission to reform Chinese tertiary education and become a top-notch international research university, SUSTech was launched in the tech capital city of Shenzhen.
SUSTech is becoming the important epicentre for China’s science and technology academic research and for the cultivation of innovative minds. The rapid ascent of SUSTech onto the global stage is remarkable. In the Times Higher Education (THE) World university Rankings 2023, it ranked 8th in Mainland China and 166th among the universities in the world. In THE Young Universities Rankings2024, SUSTech was ranked 1st in China.
The SUSTech campus sits in the rolling hills of Nanshan District, with the verdant green lawns reflecting the environmentally friendly policies of the university. The natural and tranquil environment combines perfectly with the modern style of Shenzhen and its convenient location. With the campus covering an area of nearly 2 square kilometers, there is plenty of room for students to cogitate and consider their research or relax and enjoy their lives on campus. With students transiting the campus on foot, by bike or utilizing our convenient electric shuttle buses, its commitment to environmental sustainability is strong.
Located in the dynamic metropolis of Shenzhen, China’s Silicon Valley, SUSTech is centred on a thriving ecosystem of entrepreneurship, innovation and research. Some 43 per cent of the total PCT patent applications in China came from Shenzhen in 2017, and the city shows no signs of slowing down. As China’s research and development centre, it is the perfect place for entrepreneurs, researchers and innovators alike to make their home alongside tech giants such as Huawei, Tencent, BYD, DJI, BJI and Mindray.
Shenzhen is also only distant 17 minutes from Hong Kong city centre by high speed train and about
an hour from Macau by ferry. The successful candidate will join the recently established AI-Theory Lab in the department of Computer Science and Engineering with world-leading expertise in bio-inspired computation.
Supervisor Bio
Professor Pietro S. Oliveto is Chair of the AI-Theory Lab at SUSTech. His main research interest is the rigorous performance analysis of bio-inspired computation techniques. He has successfully supervised PhD projects on the theoretical foundations of evolutionary computation, artificial immune systems, hyper-heuristics and automatic algorithm configurators. For more info on the Theory of AI Lab: https://peteroliveto.github.io/
Who: Pietro S. Oliveto, olivetop@sustech.edu.cn
When: Until 2026-01-14 16:00
Presented at next GECCO?: no
PhD: PhD in Time complexity Analysis of Bio-Inspired Computation
Where: Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China
What:
Bio-inspired meta-heuristics, such as evolutionary algorithms, genetic algorithms or artificial immune systems are general purpose algorithms that mimic powerful mechanisms from nature such as the natural evolution of species or the collective intelligence of animals with the goal of solving complex optimisation problems. They have been applied successfully to a broad range of problems in various disciplines with remarkable success. They are particularly useful in settings where limited knowledge about the problem is available (black-box optimisation) and evaluating candidate solutions is the only means of learning about the problem at hand.
In recent years theoretical analyses have emerged that provide mathematically proven statements regarding the performance of bio-inspired algorithms. They rigorously estimate the expected time required by the algorithms to find a satisfactory solution for various optimisation problems. Such analyses use mathematical techniques drawn and extended from the fields of randomised algorithms, probability theory and computational complexity. The results allow for insights into the working principles of bio-inspired meta-heuristics, enable the assessment of parameter choices and design aspects, and ultimately guide towards the design of more powerful algorithms. This studentship offers a valuable opportunity to work within this very active, challenging and exciting field of research at the intersection between theoretical computer science and artificial intelligence.
The successful applicant will perform high quality research in the area of time complexity analysis at the interface between bio-inspired computation and artificial intelligence. During the PhD studies, he/she will develop expertise in one or more promising research areas of his/her choice in this wide research area.
Possible topics include the performance analysis of:
a) Population-based meta-heuristics: highlighting their advantages over single-trajectory algorithms and/or the advantages of recombination over mutation-only algorithms;
b) Algorithm configurators: how to evolve the optimal parameter settings for the meta-heuristic;
c) Hyper-heuristics: how to evolve the meta-heuristic itself;
d) Genetic programming: how to evolve computer programs effectively.
Candidate Requirements
Applicants must have recently completed a first class (or equivalent) undergraduate degree or a Masters degree with distinction in Computer Science or close to completion. Outstanding applicants from Mathematics are also encouraged to apply. The successful applicant must have excellent analytical and computational skills. He/She must be an excellent team player who can work independently and communicate well with others. If English is not their first language, they must have an IELTS score of 6+ overall, with no less than 6.0 in each component or TOEFL of 75+. Since the project is theoretically challenging, strong mathematical and probability theory skills are required. This position is open to all qualified candidates independent of nationality.
Funding and Eligibility
This studentship covers the full tuition fee and provides a tax-free stipend from RMB 150,000 to RMB 200,000 (approx. GBP 15,000/EUR 17,000 to GBP 20,000/EUR 23,600) per annum for four years. The studentship also covers free on- campus accommodation and subsidised on-campus meals in over 10 different cafeterias/canteens as well as popular western and eastern fast food chains. Funding is available for conference attendance and collaborative research visits to related organisations world-wide. The AI-Theory Lab at SUSTech maintains effective collaborations with all the research organisations with major expertise in the theory of bio-inspired computation world-wide.
About the University/Department
Established in 2010 with the mission to reform Chinese tertiary education and become a top-notch international research university, SUSTech was launched in the tech capital city of Shenzhen.
SUSTech is becoming the important epicentre for China’s science and technology academic research and for the cultivation of innovative minds. The rapid ascent of SUSTech onto the global stage is remarkable. In the Times Higher Education (THE) World university Rankings 2023, it ranked 8th in Mainland China and 166th among the universities in the world. In THE Young Universities Rankings2024, SUSTech was ranked 1st in China.
The SUSTech campus sits in the rolling hills of Nanshan District, with the verdant green lawns reflecting the environmentally friendly policies of the university. The natural and tranquil environment combines perfectly with the modern style of Shenzhen and its convenient location. With the campus covering an area of nearly 2 square kilometers, there is plenty of room for students to cogitate and consider their research or relax and enjoy their lives on campus. With students transiting the campus on foot, by bike or utilizing our convenient electric shuttle buses, its commitment to environmental sustainability is strong.
Located in the dynamic metropolis of Shenzhen, China’s Silicon Valley, SUSTech is centred on a thriving ecosystem of entrepreneurship, innovation and research. Some 43 per cent of the total PCT patent applications in China came from Shenzhen in 2017, and the city shows no signs of slowing down. As China’s research and development centre, it is the perfect place for entrepreneurs, researchers and innovators alike to make their home alongside tech giants such as Huawei, Tencent, BYD, DJI, BJI and Mindray.
Shenzhen is also only distant 17 minutes from Hong Kong city centre by high speed train and about
an hour from Macau by ferry. The successful candidate will join the recently established AI-Theory Lab in the department of Computer Science and Engineering with world-leading expertise in bio-inspired computation.
Supervisor Bio
Professor Pietro S. Oliveto is Chair of the AI-Theory Lab at SUSTech. His main research interest is the rigorous performance analysis of bio-inspired computation techniques. He has successfully supervised PhD projects on the theoretical foundations of evolutionary computation, artificial immune systems, hyper-heuristics and automatic algorithm configurators. For more info on the Theory of AI Lab: https://peteroliveto.github.io/
Who: Pietro S. Oliveto, olivetop@sustech.edu.cn
When: Until 2026-01-14 16:00
Presented at next GECCO?: no