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Research Scientist | PhD | ML Technical Lead | ML Advisor | Machine Learning Engineer
Dr. Francois Luus previously as Technical Lead in Machine Learning at IBM Research - Africa was responsible for research programmes in Advanced and Applied AI that solve difficult problems in the domains of Radio Astronomy, Computer Vision, and Neuro-Symbolic AI. He led a multi-year joint study with the Square Kilometre Array South Africa to develop machine learning for mitigation of radio frequency interference. Filed several joint patents and published on Generative ML solutions to RFI detection and removal. He was also a principal advisor to the SETI Institute in the areas of Cloud Computing and Cognitive Computing, where he has spearheaded analytics efforts for numerous observation campaigns. He has recently been working on developing Neuro-Symbolic AI for Natural Language Understanding, e.g. for use in chatbot technology. Consulting scientist and ML specialist for the SETI Institute since 2015, and previous advisor to the NASA Frontier Development Lab. Known for developing spectrogram folding 10x faster than existing SETI Institute solutions at the time. Contributed a powerful interactive supervision platform to TensorFlow to significantly reduce the data labelling expense - used for labeling SETI signals. Advised and supported several Trappist-1 surveys with the SETI Institute, and performed various Big Data processing on TB-scale measurements. Previously, Francois was a consultant at the Remote Sensing Research Unit (CSIR, Meraka) where he developed robust domain adaptation for machine learning applied to land-use classification. He was also a researcher at the Sentech Chair in Broadband Wireless Multimedia Communications where he pioneered new information theoretic coding schemes for fast wireless networks. Francois has a PhD that focused on applied machine learning in remote sensing and previously completed a B.Eng (Computer Engineering) and M.Eng (Electr
University of Pretoria
Doctor of Philosophy (PhD), Electronic Engineering
January 1, 2012 – January 1, 2016
University of Pretoria
M.Eng (Cum Laude), Electronic Engineering
January 1, 2009 – January 1, 2011
University of Pretoria
B.Eng (Hons) (Cum Laude), Electronic engineering
January 1, 2008 – Present
University of Pretoria
B.Eng (Cum Laude), Computer Engineering
January 1, 2004 – January 1, 2007
ML Industry
Research Engineer
September 1, 2023 – Present
Stealth venture (ML research)
Senior ML Engineer
November 1, 2021 – August 1, 2023
IBM Research
Sub-Theme Lead (Neuro-Symbolic AI)
May 1, 2021 – October 1, 2021
University of the Free State
Academic Advisory Board (Computer Science)
October 1, 2019 – October 1, 2021
IBM Research
Technical Lead
May 1, 2019 – April 1, 2021
SKA South Africa
Advisor (Machine Learning - IBM Research)
January 1, 2016 – June 1, 2018
SETI Institute
Advisor (ML and Big Data Analytics - IBM Research)
September 1, 2015 – February 1, 2018
IBM Research
Research Scientist
July 1, 2015 – April 1, 2019
BitFuture
Co Founder
January 1, 2014 – January 1, 2015
University of Pretoria
Teaching assistant - ERS220 Digital Systems
January 1, 2012 – December 1, 2013
Meraka Institute, CSIR
Consultant researcher
July 1, 2011 – June 1, 2015
Defero Training
Software Engineer
April 1, 2009 – June 1, 2009
Custom Power Solutions
Electronic engineer
November 1, 2007 – August 1, 2009
Sentech Chair in Broadband Wireless Multimedia Communications
Wireless Communications Researcher
January 1, 2007 – December 1, 2011
Pieter Barnard Enterprises
Electronic engineer
November 1, 2006 – March 1, 2007
PWL Civil Consultants cc
Network Consultant
January 1, 2005 – June 1, 2015
First Patent File
IBM
June 24, 2026 – Present
Plateau
IBM
June 24, 2026 – Present
VHDL
Etion Create
June 24, 2026 – Present
CCNP
University of Pretoria - Cisco Regional Academy
June 24, 2026 – Present
Patent Issuance
IBM
June 24, 2026 – Present
CCNA
University of Pretoria - Cisco Regional Academy
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily skewed towards advanced research and development in AI/ML, with a strong academic foundation. While this demonstrates intellectual curiosity and a drive for innovation, the target role of 'Data Analyst' might be a step down in terms of complexity or focus compared to their previous roles as 'Research Engineer' or 'Senior ML Engineer'. The diversity of projects, from radio astronomy data analysis to neuro-symbolic AI, shows adaptability. However, the lack of explicit project details for the target role's specific requirements (e.g., business intelligence, dashboarding, specific data visualization tools) could indicate a potential mismatch in day-to-day responsibilities and expectations.
Soft Skills & Operational Fit
The candidate's extensive experience in research, advisory, and leadership roles at institutions like IBM Research and SETI Institute suggests strong analytical thinking, problem-solving, and communication skills. Their involvement in academic advisory boards and teaching assistant roles also indicates a capacity for collaboration and knowledge transfer. The descriptions highlight initiative (e.g., developing spectrogram folding 10x faster, contributing to TensorFlow), which points to a proactive operational fit.