Dedy Loebis

I would like to take this opportunity to introduce myself. My name is Dedy Loebis, and I am an Individual Leave to Remain (ILR) holder who has been living in the UK since 1998. I hold a BSc, MBA, MSc, and PhD, all of which reflect my commitment to education. 

I posses a BSc Electronics Engineering and an MSc in Automatic Control and Systems Engineering. I possess a PhD in Control Systems Engineering, with a focus on Artificial Intelligence, particularly in their application to Multisensor Data Fusion for the GNC of the Hammerhead Autonomous Underwater Vehicle (AUV). I dedicated a total of 7 years to research and 4 years to teaching in the relevant fields.

I possess extensive experience in conducting high-quality academic research funded by the EPSRC throughout my PhD, which centered on applying Fuzzy Logic to the navigation of an AUV. This EPSRC project involved the integration of data from multiple sensors with a vision sensor that captures underwater imagery. Furthermore, my Postdoctoral research at Cambridge University has equipped me with specialized knowledge in deep learning, especially regarding the application of Neural Networks for leak detection in water distribution systems. 

In the Post Doctoral Project, both supervised and unsupervised learning techniques were employed to detect leaks within the water distribution system. For supervised learning, segments of a time series indicating leak occurrences were labelled, allowing a neural network to be trained to recognize the associated patterns. Conversely, in unsupervised learning, the instances of leaks were concealed, and the neural network was trained to discover models capable of identifying anomalies within the time series.

I have been hired to manage and oversee Google Cloud resources for a retail and courier enterprise. Proficiency in hardware including NI Compact Field Point, NI Compact Rio, and Arduino. Expertise in programming for neural network modeling utilizing Matlab/Simulink, Labview and their associated toolboxes, along with subsequent experience in PyTorch. Experience utilizing Matlab/Simulink and Labview along with their related toolboxes, as well as Python and its corresponding libraries.

I have participated in specialized conferences focused on AUV and ROV, effectively conveying complex information to this community. I have authored award-winning journals and contributed to specialized conference papers. Capability to diligently work autonomously in order to adhere to stringent deadlines. Establishing connections with new individuals and collaborating effectively within a team has been one of my greatest strengths.

I have been involved in two EPSRC Projects and one EU Frameworks project. Two distinct projects will address your desirable criterion. In my PhD research, I integrated computer vision with INS/GPS data to achieve precise positioning of underwater objects. For my postdoctoral project at Cambridge, I employed neural networks to detect abrupt variations in water pressure and flow that could result in pipe leaks or bursts. I have experience in multi-modal data fusion and the adaptation of AI techniques for practical applications utilizing embedded systems.

I hope my background and skills align with your needs. Residing in Cambridge, I am keen to discuss potential interview opportunities with you at your earliest convenience.

I possess extensive experience in conducting high-quality academic research funded by the EPSRC throughout my PhD, which centered on applying Fuzzy Logic to the navigation of an AUV. This EPSRC project involved the integration of data from multiple sensors with a vision sensor that captures underwater imagery. Furthermore, my Postdoctoral research at Cambridge University has equipped me with specialized knowledge in deep learning, especially regarding the application of Neural Networks for leak detection in water distribution systems. 

In the Post Doctoral Project, both supervised and unsupervised learning techniques were employed to detect leaks within the water distribution system. For supervised learning, segments of a time series indicating leak occurrences were labelled, allowing a neural network to be trained to recognize the associated patterns. Conversely, in unsupervised learning, the instances of leaks were concealed, and the neural network was trained to discover models capable of identifying anomalies within the time series.

I have been hired to manage and oversee Google Cloud resources for a retail and courier enterprise. Proficiency in hardware including NI Compact Field Point, NI Compact Rio, and Arduino. Expertise in programming for neural network modeling utilizing Matlab/Simulink, Labview and their associated toolboxes, along with subsequent experience in PyTorch. Experience utilizing Matlab/Simulink and Labview along with their related toolboxes, as well as Python and its corresponding libraries.

I have participated in specialized conferences focused on AUV and ROV, effectively conveying complex information to this community. I have authored award-winning journals and contributed to specialized conference papers. Capability to diligently work autonomously in order to adhere to stringent deadlines. Establishing connections with new individuals and collaborating effectively within a team has been one of my greatest strengths.

I have been involved in two EPSRC Projects and one EU Frameworks project. Two distinct projects will address your desirable criterion. In my PhD research, I integrated computer vision with INS/GPS data to achieve precise positioning of underwater objects. For my postdoctoral project at Cambridge, I employed neural networks to detect abrupt variations in water pressure and flow that could result in pipe leaks or bursts. I have experience in multi-modal data fusion and the adaptation of AI techniques for practical applications utilizing embedded systems.

I hope my background and skills align with your needs. Residing in Cambridge, I am keen to discuss potential interview opportunities with you at your earliest convenience.

I hold a BSc, MBA, MSc, and PhD, all of which reflect my commitment to education. 

I posses a BSc Electronics Engineering and an MSc in Automatic Control and Systems Engineering. I possess a PhD in Control Systems Engineering, with a focus on Artificial Intelligence, particularly in their application to Multisensor Data Fusion for the GNC of the Hammerhead Autonomous Underwater Vehicle (AUV). I dedicated a total of 7 years to research and 4 years to teaching in the relevant fields.