
Muhammad Hassan Riaz Bhatti
Fähigkeiten und Kenntnisse
Werdegang
Berufserfahrung von Muhammad Hassan Riaz Bhatti
- Bis heute 3 Jahre, seit Aug. 2022
Firmware Developer
aSpect Systems GmbH
- Experience in firmware development for ASICs and FPGAs using VHDL and Vivado. - Involved in the design, development, and testing of firmware. - Experience in debugging and troubleshooting firmware issues and familiar with software development tools such as version control, testing frameworks and debugging tools
- 8 Monate, Feb. 2021 - Sep. 2021
Project Engineer
Techno One Pakistan
- Recommend modifications to existing prototypes based on performance in quality tests. - Designing, developing and on-site installation/configuration of access control panels and electric barriers. - Design and develop new IoT based residential and commercial security access control systems. - Supervise technical staff, ensuring that customer demands & regulations are being followed. - Analyze system requirements and customer needs to determine feasibility of project and develop system plan.
- 2 Monate, Jan. 2019 - Feb. 2019
Operations Engineer
Lahore Electric Supply Company Limited (LESCO)
Worked at 132-KV Saidpur Grid Station and I was responsible for the following tasks: - Observe voltage transformers and circuit breakers - Monitor distribution equipment and record readings on hourly basis. - Monitor the status of transmission circuits and connections with substations. - Anticipate changes in power needs caused by weather. - Operating feeders for scheduled load shedding & troubleshooting
Ausbildung von Muhammad Hassan Riaz Bhatti
- Bis heute 3 Jahre und 10 Monate, seit Okt. 2021
Nanoelectronics System
Technische Universität Dresden
- 4 Jahre und 1 Monat, Feb. 2016 - Feb. 2020
Bachelor of Engineering (Honours) in Electrical Engineering
Curtin University
Activities and societies: IEEE, ISA (International Student Association)Activities and societies: IEEE, ISA (International Student Association) My bachelors thesis, "Power Disaggregation for Smart Grid Applications", considered different clustering ML algorithms (K-means and KNN) for power disaggregation. The objective was to suggest the better suited ML algorithm in order to disaggregate household electrical loads.
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Englisch
Fließend
Deutsch
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