Senior Software Engineer : Machine Learning Ref : MAC / / SSEML Measurement And ControlThe Measurement and Control Division (MCD) is looking for a Senior Software Engineer : Machine Learning to develop artificial intelligence solutions for the minerals processing industry and contribute to the development and maintenance of the StarCS platform and related software.
The researcher will work within the software team, contributing to the development of new products for the South African and global minerals and metals industry.
Identify opportunities for technology development and commercialization; and,Keep up-to-date at a professional level with technology and business developments that are relevant to the divisions interests.
QUALIFICATIONS AND EXPERIENCE : MSc in Computer Science, Applied Mathematics, Engineering (Electrical / Mechanical / Chemical / Metallurgical, Applied Mathematics, Computer Science Must have demonstrated the ability to tackle programming in machine learning.
Ideal : PhDAt least 3 years of relevant work experience. Machine learning and minerals processing work experience is advantageous.
Machine learning (supervised and unsupervised machine learning)Academic writing, ability to present (evidenced by publication record)English-speakingPython programming languageC++ or C#The following skills / knowledge will be advantageous : Reinforcement learning experience (Temporal Difference algorithms, Bandit algorithms, Deep Reinforcement Learning, etc.
Strong statistics backgroundProcess control or other control systems knowledge a bonusPhD in a machine learning field.NETCOMPETENCIES : Excellent communication and presentation skillsExcellent numerical and quantitative skillsSelf-drivenPeople skillsAbility to multi-taskAbility to get things doneAbility to lead by exampleFurnace ControlProcess ModellingSALARY PACAGE : NEGOTIABLE CLOSING DATE : 10 AUGUST The above-mentioned vacancy is also available on the Mintek website atMintek is an equal opportunity, affirmative action employer, whose aim is topromote representativity in all levels of occupational categories