Mintek bursary advert – Reinforcement Learning in Metallurgy and Minerals Processing ( Randburg)
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Mintek bursary advert – Reinforcement Learning in Metallurgy and Minerals Processing ( Randburg)


Mintek bursary (BUR/2020/02 )


Overview

Reference BUR/2020/02

Salary ZAR/month

Job Location - South Africa -- Johannesburg Metro -- Johannesburg -- Randburg

Job Type Temporary

Posted Wednesday, February 26, 2020

Closing date 20 Mar 2020 23:59



MINTEK, South African national mineral research organisation, is one of the world’s leading technology organisation specialising in mineral processing, extractive metallurgy and related areas. Working closely with industry and other R&D institutions, Mintek provides service testwork, process development and optimisation, consulting and innovative products to clients world-wide.


Applications are invited from interested and suitably qualified persons for appointment to the position of:


Mintek bursary advert – Reinforcement Learning in Metallurgy and Minerals Processing


Ref: BUR/2020/02


Mintek is embarking on an exciting research project to develop technologies for the metallurgical industry, including minerals processing, using reinforcement learning. As part of this project, Mintek is making available bursaries for Masters and Doctoral studies into the applications of reinforcement learning within metallurgy. The combination of reinforcement learning and deep learning has led to amazing breakthroughs in robotics and artificial intelligence. The field of metallurgy is lagging behind in this trend, yet there are significant opportunities to improve the profitability of metallurgical plants by using reinforcement learning.


PURPOSE OF THE POSITION:


Successful applicants will conduct applied and theoretical research at Mintek and a South African university into the use of reinforcement learning in metallurgical processes and metallurgical plants. Examples of such research topics are given below, although the scope is not limited to these topics and prospective bursars are welcome to motivate related alternatives.


• Model-based reinforcement learning for optimal control.


• Real-time optimization of individual metallurgical processes, e.g. flotation, milling and gold leaching.


• Control system tuning via reinforcement learning.


• Safe exploration and training in metallurgical processes.


Successful applications are also expected to conduct monthly progress meetings involving both their university and Mintek appointed supervisors. Where the students are located outside of Gauteng, alternative arrangements such as video conferencing will be considered. Students studying at a university in Gauteng will be expected to spend half of their time located at Mintek and embedded within the research project. The aim is to facilitate rapid knowledge sharing between all team members.


JOB KNOWLEDGE /SKILLS REQUIRED:


• Prior research in reinforcement learning, deep learning or machine learning is advantageous.


• Formal exposure to software development is required, preferably in Python.


QUALIFICATIONS:


• The candidates must have completed an undergraduate engineering degree with excellent academic record in chemical, electrical, mechanical or metallurgy engineering.


• Studying towards their Masters or PhD degree studies focusing on the applications of reinforcement learning within metallurgy.


THE BURSARY WILL COVER:


• Full tuition/including registration;


• Accommodation and Meals fees;


• Laptop Allowance;


• Text Book Allowance



CLOSING DATE: 20 March 2020


The above-mentioned vacancy is also available on the Mintek website at www.mintek.co.za. Please apply on the Mintek website at www.mintek.co.za


This opportunity is open to South African citizens only. Shortlisted candidates will be required to undergo interviews and will be required to write a psychometrics’ test.


Should you not hear from us within one (1) month of the closing date, consider your application to be unsuccessful.


Mintek is an equal opportunity, affirmative action employer, whose aim is to promote representivity in all levels of occupational categories.



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