Loading...
 

Job Ad Display

Return to the list

PhD: Genetic programming for big data analytics
Where: University of Technology Sydney, Australia Australia
What: Location: University of Technology Sydney – City campus. UTS is #1 young university in Australia in 2019 based on QS and THE ranking. UTS is ranked #29 globally and #1 in Australia for the subject area of Computer Science and Engineering (based on 2019 ARWU rankings)

Project Detail: This project is about extending Genetic Programming in order to deal with challenging problems including large scale problems. Several topics are going to be investigated in this project including hybrid approaches, information theory, cloud computing, etc. The researcher needs to code genetic programming during this project.

Requirements: A solid background in computer and data sciences as well as strong programming skill (MatLab/Python/Java). A master degree in computer science, data science, mathematics, AI, engineering, or related field. Basic statistics knowledge and familiar with genetic programming and evolutionary computation topics.

Who: https://www.uts.edu.au/staff/amirhossein.gandomi
When: Until 2020-07-30 10:00
Presented at next GECCO?: yes

PhD: Constrained and black-box optimisation using population-based algorithms
Where: University of Technology Sydney, Australia Australia
What: Location: University of Technology Sydney – City campus. UTS is #1 young university in Australia in 2019 based on QS and THE ranking. Also, UTS is ranked #29 globally and #1 in Australia for the subject area of Computer Science and Engineering (based on 2019 ARWU rankings)

Project Details: This project is about using population-based and black-box algorithms for solving challenging optimization problems. Several topics are going to be investigated in this project including constrained optimization, multi-objective optimisation, and surrogate models.

Requirements: A solid background in computer science and optimisation as well as strong programming skills (MatLab/Python). A master degree in computer science, data science, mathematics, AI, engineering, or related field. Basic statistics knowledge and familiar with Bayesian Optimization. Background in evolutionary computation topics like multi-objective and constrained optimization. Publications with major conferences and/or journals.

Who: https://www.uts.edu.au/staff/amirhossein.gandomi
When: Until 2020-07-30 10:00
Presented at next GECCO?: yes

PhD: Improving deep and machine learning using second-order information
Where: University of Technology Sydney, Australia Australia
What: Location: University of Technology Sydney – City campus. UTS is #1 young university in Australia in 2019 based on QS and THE ranking. Also, UTS is ranked #29 globally and #1 in Australia for the subject area of Computer Science and Engineering (based on 2019 ARWU rankings)

Project Detail: This project is about incorporating second-order information into machine learning methods in order to boost the initialization and learning processes. From the machine learning methods in this project, it particularly focuses on deep neural networks. Several topics are going to be investigated in this project including hybrid approaches, big data analytics, graph theory, large scale problems.

Requirements: A solid background in data and computer sciences as well as strong programming skill (MatLab/Python). A master degree in data science, computer science, mathematics, AI, engineering, or related field. Background in statistics and machine learning methods, deep learning in particular. Publications with major conferences and/or journals.

Who: https://www.uts.edu.au/staff/amirhossein.gandomi
When: Until 2020-07-30 10:00
Presented at next GECCO?: yes

research engineer in academia: Research internship - Effective metaheuristics for the structural resolution of new zeolites
Where: IRIMAS Institute, Mulhouse, France France
What: As an intern, your work will consist in proposing and developing an improved optimization algorithm and objective function, based on the work carried out previously. Since this work is at the interface between the fields of optimization and chemistry of materials, you will discuss with chemists to familiarize yourself with the field of application, and to fully understand all aspects of the problem.

Please find, below, a link to a complete description of this postgraduate internship position to be filled as soon as possible, as part of a research project focused on optimization and metaheuristics:

http://www.lmia.uha.fr/AAP_SINPTA_EN.pdf


Who: julien.lepagnot@uha.fr
When: Until 2020-01-31 18:00
Presented at next GECCO?: no

PhD: EPSRC-funded ICASE PhD project with UoM and IBM on "Tuning Bayesian Optimization for Problems with Dynamic Resource Constraints"
Where: The University of Manchester, United Kingdom United Kingdom
What: More details about the project and supervisory team can be found in the official project advert https://www.jobs.ac.uk/job/BYV319/epsrc-ibm-industrial-case-phd-studentship-in-data-driven-optimization-tuning-bayesian-optimization-for-problems-with-dynamic-resource-constraints

Who: Richard Allmendinger, richard.allmendinger@manchester.ac.uk
When: Until 2020-04-17 00:00
Presented at next GECCO?: yes

PhD: Multi-label classification for health data: Optimization approaches for interpretable results
Where: University of Lille (Fr) / Kent University (GB), France France
What: A PhD position in Co-tutelle between U. Lille and U. Kent funded by I-Site university Lille Nord Europe on "Multi-label classification for health data: Optimization approaches for interpretable results" is open.
This Phd will be a cooperation between the ORKAD team from CRIStAL Laboratory in Lille (Pr. C. Dhaenens and Pr. L. Jourdan) and the The Computational Intelligence group, School of Computing, University of Kent, United Kingdom (Pr. A. Freitas).
The PhD candidate will be principally attached to the research team of the promotor in Lille, but in addition, the PhD candidate must spend at least one year in the research unit of the University of Kent supervisor during the three years of the doctorate. Following a successful doctoral defense, the PhD candidate will obtain a doctoral degree from each institution.

The applicant should hold a master’s degree or equivalent (minimum of 5 years of post-secondary education) before the start of the doctorate.
There are no restrictions on nationality or age. The applicant should have an English level of B2 or higher (Common European Framework of Reference for Languages (CEFRL)) at the start of the PhD.

Applicants must submit their application (in English) by email before 19th of April to the three contact persons indicated below
• Clarisse DHAENENS, Full Professor, ORKAD team (CRIStAL / University of Lille) clarisse.dhaenens at univ-lille.fr
• Laetitia JOURDAN, Full Professor, ORKAD team (CRIStAL / University of Lille) laetitia.jourdan at univ-lille.fr
• Alex FREITAS, Professor, School of Computing, University of Kent. A.A.Freitas at kent.ac.uk

More information may be found in the following link (scientific context, objectives of the thesis as well as the detailed application procedure)
https://cristal.univ-lille.fr/sujets-these/details.html?id=3253cfa693054fe591136fc5472869c6

Who: Clarisse Dhaenens - Clarisse.Dhaenens@univ-lille.fr
When: Until 2020-04-18 18:00
Presented at next GECCO?: no

PhD: Possibility to conduct doctoral studies in the Multiobjective Optimization Group in Finland
Where: University of Jyvaskyla, Faculty of Information Technology, Finland Finland
What: Are you interested in conducting doctoral studies at the Faculty of Information Technology, University of Jyvaskyla in Finland. The application deadline is April 30, 2020. For more information, see the announcement below.

The “specialization option Applied Mathematics and Computational Sciences” mentioned in the call include research conducted in the Multiobjective Optimization Group (www.mit.jyu.fi/optgroup) where multiobjective optimization, decision/prescriptive analytics and data-driven optimization (including machine learning and artificial intelligence) are of interest. In this Group, we develop, implement and apply multiobjective optimization methods, in particular, interactive methods. We work with both methods with theoretical foundations and heuristics like evolutionary algorithms and tackle challenges of real (both simulation-based and data-driven) problems. We are interested in both methods and various application areas of decision support, visualization techniques and quality indicators. We are also actively involved in the thematic research area DEMO – Decision Analytics utilizing Causal Models and Multiobjective Optimization (www.jyu.fi/demo).
If you are highly motivated in conducting doctoral studies relevant to these areas, you are most welcome to apply!
Note that being selected to conduct doctoral studies does no guarantee funding. There is some funding available to be applied separately (see below).

Doctoral studies in Information Technology at JYU, Finland

The Faculty of Information Technology at the University of Jyväskylä (JYU), Finland, provides high-quality postgraduate education based on research carried out in nine highly valued international research groups led by top researchers. On annual basis, we admit ca. 35-40 doctoral students to our postgraduate programmes and we have ca. 200 active doctoral students in the faculty, of which ca. 30 % are international students. Our undergraduate teaching programmes are the 2nd biggest in Finland. Our doctoral degree gives graduates the competence for an international research career and challenging specialist assignments in society, including diverse jobs in industry and education. Our graduates place well in the Academia, and many find opportunities in information technology and software industry.

The doctoral degrees awarded are Doctor of Philosophy and Doctor of Science (Economics and Business Administration). The doctoral studies are done in one of the following specialization options:
• Information Systems (Ph.D. or D.Sc.)
• Applied Mathematics and Computational Sciences (Ph.D.)
• Software and Communications Engineering (Ph.D.)
• Cognitive Science (Ph.D.)
• Learning Science (Ph.D.)

Application rounds twice a year, in spring and in autumn.

NEXT APPLICATION ROUND: 1 April - 30 April at 15:00 (GMT+3)
Begin preparing your application well in time before the deadline: You need to have at least two committed supervisors before submitting the actual application, and you need to prepare a research plan for your application.

Read more: How to apply https://www.jyu.fi/it/en/research/doctoral_school/how-to-apply

Please note that selection to doctoral studies does not guarantee funding. Funding decisions are made separately. For more information, please see: Funding for Doctoral Studies https://www.jyu.fi/it/en/research/doctoral_school/guide-for-doctoral-studies/funding

Doctoral School of the Faculty of Information Technology https://www.jyu.fi/it/en/research/doctoral_school is part of the University of Jyväskylä Graduate School for Doctoral Studies https://www.jyu.fi/en/research/doctoral-studies

Please, forward this announcement to those who might be interested in the positions. Do not hesitate to contact me if you have questions.

With best regards, Kaisa Miettinen
************************************
Professor Kaisa Miettinen, PhD
University of Jyvaskyla
Multiobjective Optimization Group: http://www.mit.jyu.fi/optgroup/
(our group name has changed!)
Faculty of Information Technology, P.O. Box 35 (Agora)
FI-40014 University of Jyvaskyla, Finland
- - -
  • Director of the thematic research area Decision Analytics utilizing Causal Models
and Multiobjective Optimization, http://www.jyu.fi/demo
- - -
tel. +358 50 3732247 (mob.)
email: kaisa.miettinen at jyu.fi
homepage: http://www.mit.jyu.fi/miettine and http://www.mit.jyu.fi/miettine/engl.html
My book: Nonlinear Multiobjective Optimization, Kluwer: http://www.mit.jyu.fi/miettine/book/

Who: Kaisa Miettinen, kaisa.miettinen@jyu.fi
When: Until 2020-04-30 09:00
Presented at next GECCO?: no



Return to the list