Save the dates! The next Workshop on Optimisation, Metric Bounds, Approximation and Transversality (WoMBaT2022) will be held 13–16 December 2022 in beautiful Perth, Western Australia. This iteration of wombat will be hosted at Curtin University by Curtin Centre for Optimisation and Decision Science. The event is expected to take place as an entirely in-person event. Registration will open and plenaries will be announced soon.

In addition, this year WOMBAT in collaboration with ARC Training Centre for Transforming Maintenance through Data Science, will host a special day (16^th December 2022) focused on the applications of optimization on planning and scheduling maintenance in the resource industry. We are very excited for this special event!

Save the dates! We look forward to welcoming you to Perth!

On behalf of the local organizing committee

Dr. Hoa T. Bui, Curtin Centre for Optimisation and Decision Science
Dr. Scott B. Lindstrom, Curtin Centre for Optimisation and Decision Science

The 2022 AMSI ACE network Optimisation course will launch on February 28, 2022. This year’s theme is “Theory and Methods of Modern Optimisation,” and the instructors are Prof. Andrew Eberhard (RMIT University) and Dr. Scott B. Lindstrom (Curtin Centre for Optimisation and Decision Science). Details and pre-enrolment quiz are currently available on the AMSI website. Please share with any students who might benefit from this opportunity.

LION15: The 15th Learning and Intelligent Optimization Conference
Athens, Greece, June 20-25, 2021
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Conference website https://lion15.sba-research.org/index.html
Submission link https://easychair.org/conferences/?conf=lion15
Extended Submission deadline March 15, 2021

The large variety of heuristic algorithms for hard optimization problems raises numerous interesting and challenging issues. Practitioners using heuristic algorithms for hard optimization problems are confronted with the burden of selecting the most appropriate method, in many cases through expensive algorithm configuration and parameter tuning. Scientists seek theoretical insights and demand a sound experimental methodology for evaluating algorithms and assessing strengths and weaknesses. This effort requires a clear separation between the algorithm and the experimenter, who, in too many cases, is “in the loop” as a motivated intelligent learning component. LION deals with designing and engineering ways of “learning” about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components.

This meeting explores the intersections and uncharted territories between machine learning, artificial intelligence, energy, mathematical programming and algorithms for hard optimization problems. The main purpose of the event is to bring together experts from these areas to discuss new ideas and methods, challenges and opportunities in various application areas, general trends and specific developments. We are excited to be bringing the LION conference in Greece for the third time.

The registration for virtual ANZIAM2021 meeting is now open at

https://www.anziam.org.au/ANZIAM+Annual+Conference+2021

In addition to live-streamed formal talks, the conference will use a platform that will enable live informal interaction among the participants outside of the virtual lecture rooms and will feature a number of social online events. The full description of these events is still to appear.

SCHOOL OF MATHEMATICS AND APPLIED STATISTICS (SMAS),
UNIVERSITY OF WOLLONGONG, AUSTRALIA
An exciting PhD scholarship is available in the School of Mathematics and Applied Statistics (SMAS) at the University of Wollongong, South Western Sydney campus, in the area of Optimisation. The title of the project is Determining the Convergence Speed of Derivative-free Optimisation Algorithms. The UOW scholarship is $28,092AUD tax-free per year for
three years full-time. Tuition fees (for up to 4 years) will be waived. The successful applicant will have the opportunity to work with both Australian and international collaborators, and extra funding may be available for conference travel. Applications are invited from domestic and international students who are able to commence PhD studies at the University of Wollongong in 2021.

https://documents.uow.edu.au/content/groups/public/@web/@eis/documents/doc/uow261302.pdf
HOW TO APPLY
If you are interested in applying for this scholarship, please contact Dr Chayne Planiden via email: chayne@uow.edu.au. Applications must include CV detailing previous education experience and academic transcripts. It is expected that the successful applicant will be available to commence this scholarship by 31 October 2021. Applications close 30 November, 2020.

Job no: 0051226

Work type: Fixed Term

Location: Parkville

Division/Faculty: Faculty of Science

Department/School: School of Mathematics and Statistics

Role & Superannuation rate: Academic – Full time – 9.5% super

Salary: Level A: $73,669 – $99,964 p.a (PhD entry level $93,120) or Level B: $105,232 – $124,958 p.. Level of appointment is subject to applicants’ qualifications and experience. 

The School of Mathematics and Statistics at the University of Melbourne has four two-year research positions available for exceptional early career researchers in the mathematical sciences, whose research has the potential to have a significant impact, either in fundamental research or toward practical applications. The School maintains activity in all areas of the mathematical sciences, and the positions can be related to any area.

For more details please refer to

http://jobs.unimelb.edu.au/caw/en/job/903384/postdoctoral-research-fellow-4-positions

ARC PhD Scholarship ($31,885pa): Switching Dynamics Approach for Distributed Global Optimisation

https://www.rmit.edu.au/students/student-essentials/information-for/research-candidates/enriching-your-candidature/grants-and-scholarships/postgraduate-by-research/switching-dynamics-approach-distributed-global-optimisation

Fast growing big-data in industrial systems makes finding optimal solutions for timely decision making more difficult. This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in big-data environments, hence resulting in a practical technology for industry applications (e.g. smart grids).

The specific objectives of this Project are:

1. Establish a switching dynamics approach for global optimisation, forming the foundation to accelerate convergence to search for optimal solutions.

2. Create an intelligent distributed global optimisation scheme with switching dynamics based multi-agent system concepts, which is scalable to big-data optimisation tasks.

This is a project funded by an Australian Research Council (ARC) Discovery Grant for three years (2021-2023), which aims to develop a breakthrough switching dynamics approach and new technology for global optimisation tasks in big-data applications.

The successful applicant will work on this project for the PhD in the School of Science at RMIT University supervised by Prof. Andrew Eberhard and carried out in collaboration with Prof. Xinghuo Yu (Electrical Engineering) at RMIT.

Qualifications

You are required to have a Bachelor degree in a relevant discipline such as Mathematical Sciences or Electrical Engineering with at least 2nd class upper honours or equivalent.  Experience in one or more areas in Nonlinear Dynamical Systems, Discontinuous Control Systems, Optimisation theory and\or Optimisation Algorithms is desirable. The applicant must have a strong background in mathematics.

Application

A CV detailing your qualifications, research experience and achievements, a statement of your suitability to this project, and contact details of two referees are to be emailed to Professor Andrew Eberhard at andy.eberhard@rmit.edu.au.  For further information, please contact Prof. Andrew Eberhard directly.