Type of position: post-doc, research position, research engineer in academia, research engineer in industry
Name: Yuan Tian
Description:
I am a PhD candidate in Computer Science at Victoria University of Wellington, New Zealand, specialising in evolutionary computation, automated heuristic design, and intelligent scheduling and optimisation.
My research focuses on using Genetic Programming (GP) hyper-heuristics to automatically design decision-making rules for complex dynamic scheduling problems across a wide range of domains, including project scheduling, resource-constrained scheduling, and other combinatorial optimisation settings under uncertainty. My recent work also explores surrogate-assisted evolutionary computation, phenotypic characterisation, and the integration of Large Language Models with knowledge learned by evolutionary algorithms.
I have experience in developing optimisation algorithms, simulation-based evaluation frameworks, surrogate models, and high-performance research software using Python and HPC environments. My broader interests include evolutionary computation, automated algorithm design, operations research, scheduling, AI-assisted optimisation, and data-driven decision making.
I am approaching the completion of my PhD and am seeking research, postdoctoral, research scientist, applied scientist, or optimisation/data science opportunities in academia or industry. I am particularly interested in roles where evolutionary computation and AI can be applied to real-world planning, scheduling, logistics, transportation, manufacturing, or other complex decision-making problems.
I am currently based in Wellington, New Zealand, and am open to opportunities in New Zealand, Australia, and internationally.
Short CV:
YUAN TIAN
PhD Candidate in Computer Science
Victoria University of Wellington, New Zealand
Research areas: Evolutionary Computation · Genetic Programming · Automated Heuristic Design · Scheduling and Optimisation · Surrogate-Assisted Evolutionary Computation · AI for Decision Making
PROFILE
PhD candidate specialising in evolutionary computation and intelligent scheduling and optimisation. My research develops Genetic Programming hyper-heuristics for automatically designing decision-making rules for complex dynamic scheduling problems. I have worked on automated heuristic design, active schedule generation, multi-tree GP, surrogate-assisted evolutionary computation, phenotypic characterisation, and knowledge-guided Large Language Models for scheduling decisions.
I am interested in both academic and applied research opportunities involving evolutionary computation, optimisation, scheduling, operations research, AI-assisted decision making, and data science.
EDUCATION
PhD in Computer Science — Victoria University of Wellington, New Zealand
Expected completion: 2026
Research focus: Automated Design of Heuristics for Dynamic Multi-Mode Project Scheduling
RESEARCH INTERESTS
Evolutionary Computation; Genetic Programming; Hyper-Heuristics; Automated Algorithm Design; Scheduling and Combinatorial Optimisation; Surrogate-Assisted Evolutionary Computation; Operations Research; Large Language Models for Decision Making; Data-Driven Optimisation.
SELECTED RESEARCH
Surrogate-Assisted Genetic Programming
Developed surrogate-assisted GP methods based on phenotypic characterisation for computationally expensive dynamic scheduling problems, including new behavioural encodings, similarity measures, preselection mechanisms, and duplicate-removal strategies.
Genetic Programming Hyper-Heuristics for Dynamic Scheduling
Developed GP-based decision rules for dynamic multi-mode project scheduling under uncertain activity durations, including active schedule generation and multi-tree GP for activity-group selection.
Evolutionary Knowledge-Guided LLM Decision Making
Investigated how heuristic knowledge learned by GP can guide Large Language Models in dynamic scheduling decisions, analysing solution quality, decision behaviour, consistency, and computational cost.
SELECTED PUBLICATIONS
Surrogate-Assisted Genetic Programming for Dynamic Multi-Mode Project Scheduling — IEEE Congress on Evolutionary Computation (CEC), 2026.
Genetic Programming with Activity Group Selection for Dynamic Multi-Mode Resource-Constrained Project Scheduling — PRICAI, 2025.
Generating Active Schedules for Multi-Mode Project Scheduling with Uncertain Durations Using Genetic Programming Hyper-Heuristics — EvoCOP / EvoStar, 2025.
Learning Scheduling Heuristics for Multi-Mode Resource-Constrained Project Scheduling Using Genetic Programming — IEEE Congress on Evolutionary Computation (CEC), 2024.
TECHNICAL EXPERTISE
Evolutionary Computation & Optimisation: Genetic Programming, Hyper-Heuristics, Multi-Tree GP, Surrogate-Assisted Evolutionary Computation, Multi-objective Optimisation
Programming & Data: Python, NumPy, pandas, SciPy, Numba, SQL, C++, C#, MATLAB
Research Computing: HPC, Slurm, Parallel Computing, Simulation, Performance Optimisation, Git, Linux
Data Analysis & Visualisation: Statistical Analysis, Machine Learning, Matplotlib, Power BI, Excel
TEACHING AND ACADEMIC EXPERIENCE
Tutor Lead and Tutor at Victoria University of Wellington, with teaching experience in Data Science, Databases, and Evolutionary Computation and Learning.
SEEKING
Research Scientist · Applied Scientist · Postdoctoral Researcher · Optimisation Scientist · Data Scientist · Operations Research / Scheduling roles
Particularly interested in applications involving planning, scheduling, logistics, transportation, manufacturing, and complex decision-making systems.
Location: Wellington, New Zealand
Availability: 2026; open to opportunities in New Zealand, Australia, and internationally
Email: yuan.tian@vuw.ac.nz
ORCID: 0000-0001-7435-1557
Web page: https://www.linkedin.com/in/yuan-tian-149ab6b4/
Other link:
General location: Worldwide, Asia, Australia and Oceania
Specific countries: Australia, Belgium, China, Croatia, Germany, Hong Kong, Japan, Netherlands, New Zealand, Sweden, Switzerland
Until when: 2026-09-01
Present at next GECCO? yes